MINI REVIEW article

Front. Neuroergonomics, 24 May 2021 | https://doi.org/10.3389/fnrgo.2021.678981

Affective Neurofeedback Under Naturalistic Conditions: A Mini-Review of Current Achievements and Open Challenges

  • 1Basic Neuroscience Division, McLean Hospital–Harvard Medical School, Belmont, MA, United States
  • 2Institut National de la Recherche Scientifique, University of Quebec, Montreal, QC, Canada

Affective neurofeedback training allows for the self-regulation of the putative circuits of emotion regulation. This approach has recently been studied as a possible additional treatment for psychiatric disorders, presenting positive effects in symptoms and behaviors. After neurofeedback training, a critical aspect is the transference of the learned self-regulation strategies to outside the laboratory and how to continue reinforcing these strategies in non-controlled environments. In this mini-review, we discuss the current achievements of affective neurofeedback under naturalistic setups. For this, we first provide a brief overview of the state-of-the-art for affective neurofeedback protocols. We then discuss virtual reality as a transitional step toward the final goal of “in-the-wild” protocols and current advances using mobile neurotechnology. Finally, we provide a discussion of open challenges for affective neurofeedback protocols in-the-wild, including topics such as convenience and reliability, environmental effects in attention and workload, among others.

1. Introduction

Neurofeedback is a sub-area of brain-computer interfaces (BCIs), in which the subject is trained to achieve voluntary control of the ongoing neural activity in brain regions or circuits (Sitaram et al., 2017; Watanabe et al., 2017; Thibault et al., 2018). For this, the user is presented with real-time feedback related to brain activity and must develop and optimize self-regulation strategies to improve the control performance (Strehl, 2014; Sitaram et al., 2017). Applications of neurofeedback range from performance optimization in sports (Mirifar et al., 2017) to clinical applications, where the main objective is to target abnormal functional structures to reduce or even eliminate symptoms (Thibault et al., 2018).

One field that can benefit from neurofeedback protocols is psychiatry (Kim and Birbaumer, 2014; Arns et al., 2017; Thibault et al., 2018). A common approach in these cases is affective neurofeedback (Linhartová et al., 2019), where the protocols target putative mechanisms of emotion regulation (Lindquist et al., 2012, 2016). Recent studies described promising results from neurofeedback training in patients with affective disorders, such as major depressive disorder (MDD) (Trambaiolli et al., 2021a). Moreover, recent studies show that neurofeedback training can lead to symptom improvements that last for weeks after intervention (Rance et al., 2018). Other observed benefits include the normalization of dysfunctional structures and plastic reorganization of intrinsic functional connectivity (Hampson et al., 2011; Scheinost et al., 2013) and directed effective connectivity (Zotev et al., 2011, 2013).

One crucial step for neurofeedback-based therapies is transferring the learned self-regulation strategies to real-life situations, outside of the overly controlled experimental setups (Brühl, 2015; Thibault et al., 2018). This approach is of particular importance to affective neurofeedback experiments since real-life scenarios most likely will include distractors, stressors, or other confounds that could affect the learned self-regulation strategies (Kadosh and Staunton, 2019). In this context, naturalistic setups are of extreme relevance to evaluate affective neurofeedback feasibility as a potential therapeutic approach. Although rare, neurofeedback and BCI experiments have been studied in the real-world (also referred to as “in-the-wild”) (Kosmyna, 2019). Moreover, virtual reality (VR) environments provide immersive, naturalistic experiences that can be used as an intermediate step toward applications in-the-wild. This approach is supported by computational (Renard et al., 2010) and instrumental (Cassani et al., 2020a) advances in the BCI field, providing the necessary tools to create neurofeedback protocols using VR.

This mini-review discusses the state-of-the-art of affective neurofeedback protocols under naturalistic conditions and open challenges in the field. For this, we first provide a brief overview of affective neurofeedback protocols using different neuroimaging modalities. We discuss the possibility of using VR as a transitional step toward experiments in-the-wild and evaluate current protocols using this feedback modality. Then, we assess the status of neurofeedback experiments outside the laboratory and discuss open challenges for developing affective neurofeedback protocols in such conditions.

2. Current Status of Affective Neurofeedback

Electroencephalography (EEG) was the first neuroimaging method used in developing affective neurofeedback protocols, with the first case reports dated from the 1990s (Rosenfeld et al., 1996; Baehr et al., 1997; Earnest, 1999). Usually, these protocols target the activity in prefrontal portions of the scalp. For example, the most common approach relies on possible asymmetries in channels over the prefrontal regions of the scalp (Choi et al., 2011; Peeters et al., 2014; Quaedflieg et al., 2016). This methodology assumes that the hyper- and hypo-activation of opposite hemispheres indicate the valence experienced during emotion regulation (Harmon-Jones et al., 2010) and may reflect symptoms in psychiatric patients (Thibodeau et al., 2006). Controlled experiments show that alpha asymmetry neurofeedback training may reduce negative mood and anxiety in healthy subjects (Quaedflieg et al., 2016; Mennella et al., 2017), as well as relieve depressive symptoms in patients with MDD (Choi et al., 2011; Wang et al., 2019). More examples of EEG-based neurofeedback include the control of alpha or beta bands over the parietal cortex (Escolano et al., 2014; Wang et al., 2019), sensorimotor rhythms (Lee et al., 2019), frequency ratios (Lee et al., 2019), among others.

With the advent of functional magnetic resonance imaging (fMRI), neurofeedback protocols were able to target more precisely the putative mechanisms of emotion regulation (Lindquist et al., 2012, 2016). In this context, different approaches were developed over the years, including protocols targeting the self-regulation of single regions of interest (ROIs) within these networks (Zotev et al., 2011; Young et al., 2014, 2017), multiple regions (Johnston et al., 2010, 2011; Linden et al., 2012; Mehler et al., 2018), or the functional connectivity between areas (Koush et al., 2017). In these cases, common targets include areas such as the lateral and medial prefrontal cortex (PFC), anterior cingulate cortex (ACC), insular cortex (IC), amygdala, among others (Linhartová et al., 2019). For instance, protocols targeting the amygdala self-regulation led to mood changes in healthy subjects (Zotev et al., 2011), and reduced symptoms in patients with MDD (Young et al., 2014, 2017). Moreover, functional connectivity reorganization was observed in these patients (Yuan et al., 2014; Young et al., 2018), as well as in subjects with post-traumatic stress disorder (PTSD) (Misaki et al., 2018). In approaches targeting multiple regions, although healthy subjects did not report changes in mood after training (Johnston et al., 2011), patients with MDD showed symptom improvement after multiple training sessions (Linden et al., 2012; Mehler et al., 2018), with benefits persisting at follow-up (Mehler et al., 2018).

The neurofeedback approach used by EEG and fMRI, although based on different methodologies, presents correlated neural mechanisms. For instance, the self-regulation of the amygdala during neurofeedback also engages brain structures from the frontal cortex (Zotev et al., 2013). Furthermore, studies with simultaneous EEG and fMRI recordings demonstrate an existing association between the amygdala self-regulation and the frontal EEG asymmetry (Zotev et al., 2016, 2018). In this context, Zotev and colleagues recently proposed a hybrid neurofeedback protocol, combining fMRI and EEG data during emotion regulation and demonstrating its feasibility with healthy subjects and MDD patients (Zotev et al., 2014, 2020; Zotev and Bodurka, 2020).

More recently, the use of functional near-infrared spectroscopy (fNIRS) has been explored (Ehlis et al., 2018; Kohl et al., 2020). Protocols targeting areas such as the dorsolateral PFC (dlPFC) (Marx et al., 2015; Hudak et al., 2017; Kimmig et al., 2019) and the orbitofrontal cortex (OFC) (Li et al., 2019) have been studied in healthy subjects and psychiatric populations, although not necessarily targeting emotion regulation. However, several studies using multivariate pattern recognition approaches demonstrated that fNIRS signals from the PFC are sufficient to decode affective processing during the visualization of pictures (Hosseini et al., 2011; Trambaiolli et al., 2018b, 2021b) and videos (Bandara et al., 2018; Hu et al., 2019), as well as during autobiographical affective imagery (Tai and Chau, 2009; Trambaiolli et al., 2018b, 2021b). In this context, Trambaiolli et al. (2018a) proposed a decoding-based affective neurofeedback protocol recalling positive autobiographical memories. Healthy participants were able to achieve satisfactory control of their prefrontal and occipital neural activity. However, further testing of this approach in clinical samples is still needed.

3. Virtual Reality as a Transitional Step

Virtual reality (VR) is an immersive three-dimensional graphical system that provides the sense of presence in the virtual world (Burdea and Coiffet, 2003). This sensation can be experienced by real-time interactions with the synthetic environment (Lotte et al., 2012). To improve the level of interaction, one may also use information from the participant, including body movements or physiological responses (Kritikos et al., 2021). This technology has been investigated as a potential therapeutic approach for neuromotor rehabilitation (Massetti et al., 2016; Ravi et al., 2017), or as an exposure therapy in psychiatry (Maples-Keller et al., 2017; Deng et al., 2019). Moreover, it also allows for investigations combining VR and neuromodulatory technologies (Cassani et al., 2020b). Even though the VR scenario emulates a real-world situation, the experimenter still has control over the environment, and the session can be stopped at any time the patient is uncomfortable (Maples-Keller et al., 2017). Thus, it provides advantages in experimental manipulation and control not previously available.

When combined with neurofeedback, VR can be used to evaluate how a less controlled environment influences the neurofeedback protocol, as well as the learned strategies. For instance, different neurofeedback protocols for attention training included a virtual classroom environment (Rohani and Puthusserypady, 2015; Hudak et al., 2017). In BCI protocols using EEG-based P300 signals, participants were able to control the system even in the presence of distracting elements (Rohani and Puthusserypady, 2015). In an fNIRS-based protocol, participants that learned how to self-regulate their hemodynamic signal in the dlPFC showed improved performance in inhibitory control tasks after training (Hudak et al., 2017). Moreover, a study comparing two-dimensional (2D) and three-dimensional (3D) VR feedback showed higher learning rates in the 3D-VR group (Berger and Davelaar, 2018).

Specific to affective neurofeedback, few protocols can be found in the literature using VR as a feedback tool. For instance, Lorenzetti et al. (2018) proposed a proof-of-concept fMRI-based experiment, with feedback provided by changing the color of the VR scenario. Participants were trained to self-regulate their neural activity in an ROI-based (amygdala) or decoding-based neurofeedback while inducing and sustaining complex emotions, such as tenderness and anguish. Although showing the technical feasibility of incorporating VR to affective neurofeedback, the color-changing scenario did not properly emulate a real-world situation.

On the other hand, the study from Aranyi et al. (2016) trained healthy participants to cheer up a virtual agent using the self-regulation of the asymmetry of fNIRS oxyhemoglobin concentrations in the dlPFC (this dataset is currently publicly available for future investigations; Charles et al., 2020). In a different study, Yamin et al. (2017) trained participants with depth electrodes in the amygdala to down-regulate this region while receiving feedback from virtual agents in a waiting room environment. In both experiments, participants were able to self-regulate the desired ROIs while receiving feedback in the VR setup. Moreover, the feedback provided is more consistent with possible situations faced by patients in real life.

4. Toward Affective Neurofeedback In-the-Wild

Neurofeedback protocols in-the-wild will allow for the continuity of the training outside of the research or clinical environment. This will ensure the reinforcement or adaptation of the developed strategies for the maintenance of long-term benefits.

MEG and fMRI have the highest spatial resolution among the most popular non-invasive neurofeedback protocols. Recent advances in the development of portable scanners (Boto et al., 2018; Wald et al., 2020; Zhang et al., 2020) bring hope to applications in the mid- and long-term future. However, the physical restraints associated with current equipment make in-the-wild experiments impractical (Sulzer et al., 2013). In this context, to increase the portability of a neurofeedback system, the use of mobile or wireless EEG and fNIRS devices emerge as a possible solution (Hondrou and Caridakis, 2012; Pinti et al., 2018; Quaresima and Ferrari, 2019). Comparative studies show that mobile EEG devices have similar accuracy to over the bench equipment (De Vos et al., 2014; Ries et al., 2014), and its applications include studying neural correlates of motor behavior (Packheiser et al., 2020), attention monitoring (Ladouce et al., 2019), and mental state monitoring in ambulatory conditions (Albuquerque et al., 2020; Parent et al., 2020). However, these devices require more pre-processing and filtering steps to compensate for biological artifacts (for instance, ocular and muscular activity) or instrumental and environmental noises (electrode misplacement, electrical, and radio-frequency interference, among others) (Fairclough and Lotte, 2020). Mobile fNIRS devices have also been tested in-the-wild, for instance, while walking (Doi et al., 2013; Mirelman et al., 2014; Maidan et al., 2016), playing table tennis (Balardin et al., 2017b), or playing violin (Vanzella et al., 2019). Similar to EEG, mobile fNIRS have to account for muscular artifacts (Balardin et al., 2017a), as well as instrumental and environmental noises such as optical decoupling and local luminance (Pinti et al., 2018). These approaches can also be combined into hybrid protocols, which have the advantage of combining multiple aspects of neural activity (for example, electric and hemodynamic changes) (Pfurtscheller et al., 2010; Müller-Putz et al., 2011, 2015). In this context, researchers have been developing mobile, and modular multimodal sensors combining EEG and fNIRS channels (von Lühmann et al., 2016).

Studies investigating the feasibility of affective neurofeedback protocols in-the-wild are still rare. However, studies using similar approaches in-the-wild, such as brain-machine interfaces (BMI), can be found in the literature. For instance, healthy participants were able to control the directions of a wheelchair based on P300 (Iturrate et al., 2009), steady-state visual evoked potentials (SSVEP) (Müller et al., 2013), or P300+SSVEP signals combined (Li et al., 2013). Moreover, using a stimulus-independent design, paraplegic patients learned to control a lower limb exoskeleton during gait using a motor imagery protocol (Donati et al., 2016). These examples provide a rationale for the possibility of experimenting with affective neurofeedback protocols in-the-wild.

5. Challenges for Affective Neurofeedback In-the-Wild

Although presenting promising results in controlled environments, current affective neurofeedback protocols face several methodological challenges for real-world applications (Kosmyna, 2019). If neglected, these aspects will become a barrier for in-the-wild setups, and cause frustration and lead to discontinued training. Due to these caveats, some of these challenges are listed, and potential solutions are discussed next.

5.1. Convenience

The physical limitations experienced during current protocols play a fundamental challenge for naturalistic experiences. For instance, fMRI protocols are physically restrictive, and the user may experience claustrophobia during the session (Sulzer et al., 2013). On the other hand, EEG- and fNIRS-based protocols may require a relatively long time for the cap preparation (positioning, conductive gel, calibration, among others). It also results in residual gel over the participant's head after the session. In this context, the use of dry and active electrodes is a possible alternative to reduce the inconvenience of the setup preparation (Lopez-Gordo et al., 2014). For instance, commercially available EEG headbands using dry electrodes were successfully employed in emotion classification in the lab (Arsalan et al., 2019), as well as during attention training neurofeedback (Kovacevic et al., 2015) and stress monitoring (Parent et al., 2020) experiments in-the-wild. Thus, these types of setups should be further explored for affective neurofeedback in-the-wild.

5.2. Feedback Modality

The feedback modality is important during reinforcement learning. For studies using VR, visual feedback seems to be an obvious choice. However, for in-the-wild applications, the best feedback approach is still an open question. One portable option would be using a laptop for real-time data processing and providing visual feedback, but other mobile devices, such as cellphones and tablets, should be explored. For instance, stimulus-dependent BCI protocols are already possible using these devices (Wang et al., 2013; Jijun et al., 2015). Although not typical for affective neurofeedback, other feedback modalities should be explored in-the-wild (Sitaram et al., 2017). For instance, haptic and auditory feedback could be integrated with current wearable technologies, such as smartwatches and headphones, respectively.

5.3. Attention and Workload Variations

Under naturalistic conditions, the neurofeedback protocol will need to consider different effects caused by the environment. For instance, environmental distractions may influence the attention, stress levels, and mental workload, leading to physiological noises such as abrupt changes in EOG and EMG signals caused by reflexive eye movements or muscular responses (Theeuwes et al., 1998; de Wied et al., 2006). Moreover, our brain is continuously processing environmental information, so involuntary neural patterns will also be caused by the environment (Engelien et al., 2000; Boly et al., 2004). As previously mentioned, multimodal approaches could be a solution combining biosignals from different sources to separate the desired neural signal from physiological noises (Pfurtscheller et al., 2010; Müller-Putz et al., 2011, 2015). Additionally, hybrid systems may be used to monitor levels of stress and mental workload to adapt the neurofeedback algorithm (Albuquerque et al., 2020; Parent et al., 2020). For instance, the study from Falk et al. (2010) evaluated the effects of physiological and auditory noises emulating real-world situations during tasks commonly used in BCI experiments. After using compensatory algorithms, participants were able to control the BCI system with performances similar to the silent conditions. In a different scenario, the incorporation of error-related potentials in the BCI algorithm also led to optimized training results (Chavarriaga et al., 2014; Spüler and Niethammer, 2015).

5.4. Algorithm Robustness

As previously described, EEG and fNIRS are the most commonly used neuroimaging modalities in-the-wild. However, these technologies are prone to non-neural physiological noises, such as electromagnetic fields for EEG and environmental light sources for fNIRS (Fatourechi et al., 2007; Strait and Scheutz, 2014; Minguillon et al., 2017). Such noise sources require robust artifact removal algorithms (Fairclough and Lotte, 2020), for example using adaptative filters (Rosanne et al., 2021), or real-time independent component analysis (ICA) (Mayeli et al., 2016; Val-Calvo et al., 2019). Additionally, these algorithms should be simple and computational cost-effective, once they will ultimately be implemented and run in portable devices.

5.5. Decoding Performance

Some neurofeedback protocols use decoding-based approaches (Taschereau-Dumouchel et al., 2020), which allow the bi-directional (up and down) self-regulation of multiple regions or channels, leading to differential plasticity changes in specific subgroups of neurons (Shibata et al., 2019). This approach may require multiple training/calibration blocks before the algorithm starts to identify the user's neural patterns. Although a recent survey shows that while the participants are comprehensive about the need for training blocks during the first session (Kosmyna, 2019), it may become a problem for long-term usage. Also, under naturalistic setups, the decoder is likely to present higher intra-session variability given the shifts in attention and stress levels, mental workload, or environmental noises, as mentioned above. In this context, alternatives such as the use of subject-independent protocols (Ray et al., 2015; Trambaiolli et al., 2021b), artificially generated training samples (Lotte, 2015), or self-recalibrating classifiers (Bishop et al., 2014) have been explored. Moreover, these approaches will be facilitated given recent efforts to create open-access big-databases from decoding-based neurofeedback (Cortese et al., 2021), or affective decoding experiments (Abadi et al., 2015; Lan et al., 2020).

5.6. Non-specific Effects

Although showing potential benefits in specific clinical populations, the results from neurofeedback training can be driven by non-specific effects (Ros et al., 2020). For instance, similar to other clinical interventions, patients under neurofeedback training may present clinical improvement due to placebo effects (Thibault and Raz, 2017; Thibault et al., 2017). Also, other areas composing large-scale networks may present variations in addition to the targeted region of interest (Mayeli et al., 2020). In the context of protocols under naturalistic conditions, the participant may show improvement in mood and anxiety symptoms based on the enriched environment or interaction with the high-tech setup. In this context, studies investigating affective neurofeedback in naturalistic setups must follow proper designs for control groups and measures (Sorger et al., 2019), and adequate reporting practices (Ros et al., 2020).

5.7. Replicability and Reproducibility

Current neurofeedback studies still lack a detailed description of online signal processing (Heunis et al., 2020), while recent checklists do not address the detailed description of offline analysis (Tursic et al., 2020). The accurate and detailed description of experimental protocols and analytical methods is crucial to ensure proper replication studies in the neurofeedback field (Melnikov, 2021). This is particularly important for experiments in-the-wild that will require extensive pipelines to deal with new types of movement and environmental noises (Fairclough and Lotte, 2020).

5.8. Neurofeedback Illiteracy

BCI illiteracy is a phenomenon in which 10–50% of BCI users will not gain voluntary control of their neural activity (Allison and Neuper, 2010; Edlinger et al., 2015). This concept can also be expanded to neurofeedback protocols (Alkoby et al., 2018). In this context, there is an increasing interest for physiological or psychological predictors describing who would benefit from neurofeedback training (Alkoby et al., 2018). For example, several studies report functional neural networks (Weber et al., 2011; Scheinost et al., 2014; Wan et al., 2014; Trambaiolli et al., 2018a) and neuroanatomical differences (Halder et al., 2013) are related to the neurofeedback literacy in both healthy and clinical populations. Also, psychological aspects as control belief, frustration, concentration, among others, play an essential role during the training performance (Alkoby et al., 2018; Kadosh and Staunton, 2019). However, studies of illiteracy predictors are mainly based on experiments under very controlled environments. How these predictors will be affected during neurofeedback training in naturalist setups remains unknown. Moreover, future investigations should identify possible differences between responders in controlled environments and responders under naturalistic setups. For instance, those undergoing VR-based training might experience cybersickness (Weech et al., 2019), which may lead to a new illiteracy category. This way, proper training, and transfer strategies will be developed for each group of users.

6. Conclusion

Affective neurofeedback has been investigated as a potential therapeutic tool in psychiatry, showing promising results in many clinical samples. To advance this technology, the study of neurofeedback protocols under naturalistic conditions is necessary. It will optimize the transference of the learned self-regulation strategies to real-world scenarios or extend the neurofeedback training outside the lab. This mini-review showed that VR setups are already being explored for affective neurofeedback protocols and can be used as a transitional step before investigations in-the-wild. Moreover, the rise of portable EEG and fNIRS devices and the successful application of these instruments for BCI protocols in-the-wild endorse the technical feasibility of affective neurofeedback in such conditions. Besides improving the strategy transference and continuous training, designs that work in-the-wild could be a solution to bring neurofeedback to patients who can not visit large imaging centers for different reasons (location, mobility limitations, among others). Moreover, these protocols and related training can be expanded to non-clinical environments, including applications in gaming, affective computing, sports performance, among others. We close with a discussion on open challenges for neurofeedback training in-the-wild, including: convenience and reliability of the neurofeedback setup, environmental effects in attention and workload, non-specific effects, and possible new neurofeedback illiteracy categories.

Author Contributions

All authors contributed equally to the intellectual efforts and writing of the paper.

Funding

This study was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC), grant number RGPIN-2016-04175.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors thank Dr. Raymundo Cassani for valuable discussions on this topic.

References

Abadi, M. K., Subramanian, R., Kia, S. M., Avesani, P., Patras, I., and Sebe, N. (2015). DECAF: MEG-based multimodal database for decoding affective physiological responses. IEEE Trans. Affect. Comput. 6, 209–222. doi: 10.1109/TAFFC.2015.2392932

CrossRef Full Text | Google Scholar

Albuquerque, I., Tiwari, A., Parent, M., Cassani, R., Gagnon, J.-F., Lafond, D., et al. (2020). WAUC: a multi-modal database for mental workload assessment under physical activity. Front. Neurosci. 14:549524. doi: 10.3389/fnins.2020.549524

PubMed Abstract | CrossRef Full Text | Google Scholar

Alkoby, O., Abu-Rmileh, A., Shriki, O., and Todder, D. (2018). Can we predict who will respond to neurofeedback? A review of the inefficacy problem and existing predictors for successful EEG neurofeedback learning. Neuroscience 378, 155–164. doi: 10.1016/j.neuroscience.2016.12.050

PubMed Abstract | CrossRef Full Text | Google Scholar

Allison, B. Z., and Neuper, C. (2010). “Could anyone use a BCI?,” in Brain-Computer Interfaces, eds D. Tan, and A. Nijholt (London: Springer), 35–54. doi: 10.1007/978-1-84996-272-8_3

CrossRef Full Text | Google Scholar

Aranyi, G., Pecune, F., Charles, F., Pelachaud, C., and Cavazza, M. (2016). Affective interaction with a virtual character through an fNIRS brain-computer interface. Front. Comput. Neurosci. 10:70. doi: 10.3389/fncom.2016.00070

PubMed Abstract | CrossRef Full Text | Google Scholar

Arns, M., Batail, J.-M., Bioulac, S., Congedo, M., Daudet, C., Drapier, D., et al. (2017). Neurofeedback: One of today's techniques in psychiatry? L'encephale 43, 135–145. doi: 10.1016/j.encep.2016.11.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Arsalan, A., Majid, M., Butt, A. R., and Anwar, S. M. (2019). Classification of perceived mental stress using a commercially available EEG headband. IEEE J. Biomed. Health Inform. 23, 2257–2264. doi: 10.1109/JBHI.2019.2926407

PubMed Abstract | CrossRef Full Text | Google Scholar

Baehr, E., Rosenfeld, J. P., and Baehr, R. (1997). The clinical use of an alpha asymmetry protocol in the neurofeedback treatment of depression: two case studies. J. Neurother. 2, 10–23. doi: 10.1300/J184v02n03_02

CrossRef Full Text | Google Scholar

Balardin, J. B., Morais, G. A. Z., Furucho, R. A., Trambaiolli, L. R., and Sato, J. R. (2017a). Impact of communicative head movements on the quality of functional near-infrared spectroscopy signals: negligible effects for affirmative and negative gestures and consistent artifacts related to raising eyebrows. J. Biomed. Opt. 22:046010. doi: 10.1117/1.JBO.22.4.046010

PubMed Abstract | CrossRef Full Text | Google Scholar

Balardin, J. B., Zimeo Morais, G. A., Furucho, R. A., Trambaiolli, L., Vanzella, P., Biazoli, C. Jr, et al. (2017b). Imaging brain function with functional near-infrared spectroscopy in unconstrained environments. Front. Hum. Neurosci. 11:258. doi: 10.3389/fnhum.2017.00258

PubMed Abstract | CrossRef Full Text | Google Scholar

Bandara, D., Velipasalar, S., Bratt, S., and Hirshfield, L. (2018). Building predictive models of emotion with functional near-infrared spectroscopy. Int. J. Hum. Comput. Stud. 110, 75–85. doi: 10.1016/j.ijhcs.2017.10.001

CrossRef Full Text | Google Scholar

Berger, A. M., and Davelaar, E. J. (2018). Frontal alpha oscillations and attentional control: a virtual reality neurofeedback study. Neuroscience 378, 189–197. doi: 10.1016/j.neuroscience.2017.06.007

PubMed Abstract | CrossRef Full Text | Google Scholar

Bishop, W., Chestek, C. C., Gilja, V., Nuyujukian, P., Foster, J. D., Ryu, S. I., et al. (2014). Self-recalibrating classifiers for intracortical brain-computer interfaces. J. Neural Eng. 11:026001. doi: 10.1088/1741-2560/11/2/026001

PubMed Abstract | CrossRef Full Text | Google Scholar

Boly, M., Faymonville, M.-E., Peigneux, P., Lambermont, B., Damas, P., Del Fiore, G., et al. (2004). Auditory processing in severely brain injured patients: differences between the minimally conscious state and the persistent vegetative state. Arch. Neurol. 61, 233–238. doi: 10.1001/archneur.61.2.233

PubMed Abstract | CrossRef Full Text | Google Scholar

Boto, E., Holmes, N., Leggett, J., Roberts, G., Shah, V., Meyer, S. S., et al. (2018). Moving magnetoencephalography towards real-world applications with a wearable system. Nature 555, 657–661. doi: 10.1038/nature26147

PubMed Abstract | CrossRef Full Text | Google Scholar

Brühl, A. B. (2015). Making sense of real-time functional magnetic resonance imaging (RTfMRI) and RTfMRI neurofeedback. Int. J. Neuropsychopharmacol. 18:pyv020. doi: 10.1093/ijnp/pyv020

PubMed Abstract | CrossRef Full Text | Google Scholar

Burdea, G. C., and Coiffet, P. (2003). Virtual Reality Technology. Hoboken, NJ: John Wiley & Sons. doi: 10.1162/105474603322955950

CrossRef Full Text | Google Scholar

Cassani, R., Moinnereau, M.-A., Ivanescu, L., Rosanne, O., and Falk, T. H. (2020a). Neural interface instrumented virtual reality headsets: toward next-generation immersive applications. IEEE Syst. Man Cybernet. Mag. 6, 20–28. doi: 10.1109/MSMC.2019.2953627

CrossRef Full Text | Google Scholar

Cassani, R., Novak, G. S., Falk, T. H., and Oliveira, A. A. (2020b). Virtual reality and non-invasive brain stimulation for rehabilitation applications: a systematic review. J. Neuroeng. Rehabil. 17, 1–16. doi: 10.1186/s12984-020-00780-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Charles, F., Martins, C. D. C., and Cavazza, M. (2020). Prefrontal asymmetry bci neurofeedback datasets. Front. Neurosci. 14:601402. doi: 10.3389/fnins.2020.601402

PubMed Abstract | CrossRef Full Text | Google Scholar

Chavarriaga, R., Sobolewski, A., and Millán, J. d. R. (2014). Errare machinale est: the use of error-related potentials in brain-machine interfaces. Front. Neurosci. 8:208. doi: 10.3389/fnins.2014.00208

PubMed Abstract | CrossRef Full Text | Google Scholar

Choi, S. W., Chi, S. E., Chung, S. Y., Kim, J. W., Ahn, C. Y., and Kim, H. T. (2011). Is alpha wave neurofeedback effective with randomized clinical trials in depression? A pilot study. Neuropsychobiology 63, 43–51. doi: 10.1159/000322290

PubMed Abstract | CrossRef Full Text | Google Scholar

Cortese, A., Tanaka, S. C., Amano, K., Koizumi, A., Lau, H., Sasaki, Y., et al. (2021). The DecNef collection, fmri data from closed-loop decoded neurofeedback experiments. Sci. Data 8, 1–9. doi: 10.1038/s41597-021-00845-7

PubMed Abstract | CrossRef Full Text | Google Scholar

De Vos, M., Kroesen, M., Emkes, R., and Debener, S. (2014). P300 speller bci with a mobile eeg system: comparison to a traditional amplifier. J. Neural Eng. 11:036008. doi: 10.1088/1741-2560/11/3/036008

PubMed Abstract | CrossRef Full Text | Google Scholar

de Wied, M., van Boxtel, A., Zaalberg, R., Goudena, P. P., and Matthys, W. (2006). Facial EMG responses to dynamic emotional facial expressions in boys with disruptive behavior disorders. J. Psychiatr. Res. 40, 112–121. doi: 10.1016/j.jpsychires.2005.08.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Deng, W., Hu, D., Xu, S., Liu, X., Zhao, J., Chen, Q., et al. (2019). The efficacy of virtual reality exposure therapy for PTSD symptoms: a systematic review and meta-analysis. J. Affect. Disord. 257, 698–709. doi: 10.1016/j.jad.2019.07.086

PubMed Abstract | CrossRef Full Text | Google Scholar

Doi, T., Makizako, H., Shimada, H., Park, H., Tsutsumimoto, K., Uemura, K., et al. (2013). Brain activation during dual-task walking and executive function among older adults with mild cognitive impairment: a fNIRS study. Aging Clin. Exp. Res. 25, 539–544. doi: 10.1007/s40520-013-0119-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Donati, A. R., Shokur, S., Morya, E., Campos, D. S., Moioli, R. C., Gitti, C. M., et al. (2016). Long-term training with a brain-machine interface-based gait protocol induces partial neurological recovery in paraplegic patients. Sci. Rep. 6, 1–16. doi: 10.1038/srep30383

PubMed Abstract | CrossRef Full Text | Google Scholar

Earnest, C. (1999). Single case study of EEG asymmetry biofeedback for depression: an independent replication in an adolescent. J. Neurother. 3, 28–35. doi: 10.1300/J184v03n02_04

CrossRef Full Text | Google Scholar

Edlinger, G., Allison, B. Z., and Guger, C. (2015). How many people can use a BCI system? Clin. Syst. Neurosci. 33–66. doi: 10.1007/978-4-431-55037-2_3

CrossRef Full Text | Google Scholar

Ehlis, A.-C., Barth, B., Hudak, J., Storchak, H., Weber, L., Kimmig, A.-C. S., et al. (2018). Near-infrared spectroscopy as a new tool for neurofeedback training: applications in psychiatry and methodological considerations. Jpn. Psychol. Res. 60, 225–241. doi: 10.1111/jpr.12225

CrossRef Full Text | Google Scholar

Engelien, A., Huber, W., Silbersweig, D., Stern, E., Frith, C. D., Döring, W., et al. (2000). The neural correlates of 'deaf-hearing' in man: conscious sensory awareness enabled by attentional modulation. Brain 123, 532–545. doi: 10.1093/brain/123.3.532

PubMed Abstract | CrossRef Full Text | Google Scholar

Escolano, C., Navarro-Gil, M., Garcia-Campayo, J., Congedo, M., De Ridder, D., and Minguez, J. (2014). A controlled study on the cognitive effect of alpha neurofeedback training in patients with major depressive disorder. Front. Behav. Neurosci. 8:296. doi: 10.3389/fnbeh.2014.00296

PubMed Abstract | CrossRef Full Text | Google Scholar

Fairclough, S. H., and Lotte, F. (2020). Grand challenges in neurotechnology and system neuroergonomics. Front. Neuroergon. 1:2. doi: 10.3389/fnrgo.2020.602504

CrossRef Full Text | Google Scholar

Falk, T. H., Guirgis, M., Power, S., and Chau, T. T. (2010). Taking NIRS-BCIs outside the lab: towards achieving robustness against environment noise. IEEE Trans. Neural Syst. Rehabil. Eng. 19, 136–146. doi: 10.1109/TNSRE.2010.2078516

PubMed Abstract | CrossRef Full Text | Google Scholar

Fatourechi, M., Bashashati, A., Ward, R. K., and Birch, G. E. (2007). EMG and EOG artifacts in brain computer interface systems: a survey. Clin. Neurophysiol. 118, 480–494. doi: 10.1016/j.clinph.2006.10.019

PubMed Abstract | CrossRef Full Text | Google Scholar

Halder, S., Varkuti, B., Bogdan, M., Kübler, A., Rosenstiel, W., Sitaram, R., et al. (2013). Prediction of brain-computer interface aptitude from individual brain structure. Front. Hum. Neurosci. 7:105. doi: 10.3389/fnhum.2013.00105

PubMed Abstract | CrossRef Full Text | Google Scholar

Hampson, M., Scheinost, D., Qiu, M., Bhawnani, J., Lacadie, C. M., Leckman, J. F., et al. (2011). Biofeedback of real-time functional magnetic resonance imaging data from the supplementary motor area reduces functional connectivity to subcortical regions. Brain Connect. 1, 91–98. doi: 10.1089/brain.2011.0002

PubMed Abstract | CrossRef Full Text | Google Scholar

Harmon-Jones, E., Gable, P. A., and Peterson, C. K. (2010). The role of asymmetric frontal cortical activity in emotion-related phenomena: a review and update. Biol. Psychol. 84, 451–462. doi: 10.1016/j.biopsycho.2009.08.010

PubMed Abstract | CrossRef Full Text | Google Scholar

Heunis, S., Lamerichs, R., Zinger, S., Caballero-Gaudes, C., Jansen, J. F., Aldenkamp, B., et al. (2020). Quality and denoising in real-time functional magnetic resonance imaging neurofeedback: a methods review. Hum. Brain Mapp. 41, 3439–3467. doi: 10.1002/hbm.25010

PubMed Abstract | CrossRef Full Text | Google Scholar

Hondrou, C., and Caridakis, G. (2012). “Affective, natural interaction using EEG: sensors, application and future directions,” in Hellenic Conference on Artificial Intelligence (Lamia: Springer), 331–338. doi: 10.1007/978-3-642-30448-4_42

CrossRef Full Text | Google Scholar

Hosseini, S. H., Mano, Y., Rostami, M., Takahashi, M., Sugiura, M., and Kawashima, R. (2011). Decoding what one likes or dislikes from single-trial fNIRS measurements. Neuroreport 22, 269–273. doi: 10.1097/WNR.0b013e3283451f8f

PubMed Abstract | CrossRef Full Text | Google Scholar

Hu, X., Zhuang, C., Wang, F., Liu, Y.-J., Im, C.-H., and Zhang, D. (2019). fNIRS evidence for recognizably different positive emotions. Front. Hum. Neurosci. 13:120. doi: 10.3389/fnhum.2019.00120

PubMed Abstract | CrossRef Full Text | Google Scholar

Hudak, J., Blume, F., Dresler, T., Haeussinger, F. B., Renner, T. J., Fallgatter, A. J., et al. (2017). Near-infrared spectroscopy-based frontal lobe neurofeedback integrated in virtual reality modulates brain and behavior in highly impulsive adults. Front. Hum. Neurosci. 11:425. doi: 10.3389/fnhum.2017.00425

PubMed Abstract | CrossRef Full Text | Google Scholar

Iturrate, I., Antelis, J. M., Kubler, A., and Minguez, J. (2009). A noninvasive brain-actuated wheelchair based on a p300 neurophysiological protocol and automated navigation. IEEE Trans. Robot. 25, 614–627. doi: 10.1109/TRO.2009.2020347

CrossRef Full Text | Google Scholar

Jijun, T., Peng, Z., Ran, X., and Lei, D. (2015). “The portable P300 dialing system based on tablet and emotiv EPOC headset,” in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (Milan), 566–569. doi: 10.1109/EMBC.2015.7318425

PubMed Abstract | CrossRef Full Text | Google Scholar

Johnston, S., Linden, D. E. J., Healy, D., Goebel, R., Habes, I., and Boehm, S. (2011). Upregulation of emotion areas through neurofeedback with a focus on positive mood. Cogn. Affect. Behav. Neurosci. 11, 44–51. doi: 10.3758/s13415-010-0010-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Johnston, S. J., Boehm, S. G., Healy, D., Goebel, R., and Linden, D. E. (2010). Neurofeedback: a promising tool for the self-regulation of emotion networks. Neuroimage 49, 1066–1072. doi: 10.1016/j.neuroimage.2009.07.056

PubMed Abstract | CrossRef Full Text | Google Scholar

Kadosh, K. C., and Staunton, G. (2019). A systematic review of the psychological factors that influence neurofeedback learning outcomes. Neuroimage 185, 545–555. doi: 10.1016/j.neuroimage.2018.10.021

PubMed Abstract | CrossRef Full Text | Google Scholar

Kim, S., and Birbaumer, N. (2014). Real-time functional MRI neurofeedback: a tool for psychiatry. Curr. Opin. Psychiatry 27, 332–336. doi: 10.1097/YCO.0000000000000087

PubMed Abstract | CrossRef Full Text | Google Scholar

Kimmig, A.-C. S., Dresler, T., Hudak, J., Haeussinger, F. B., Wildgruber, D., Fallgatter, A. J., et al. (2019). Feasibility of NIRS-based neurofeedback training in social anxiety disorder: behavioral and neural correlates. J. Neural Trans. 126, 1175–1185. doi: 10.1007/s00702-018-1954-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Kohl, S. H., Mehler, D., Lührs, M., Thibault, R. T., Konrad, K., and Sorger, B. (2020). The potential of functional near-infrared spectroscopy-based neurofeedback96a systematic review and recommendations for best practice. Front. Neurosci. 14:594. doi: 10.31234/osf.io/yq3vj

PubMed Abstract | CrossRef Full Text | Google Scholar

Kosmyna, N. (2019). “Brain-computer interfaces in the wild: lessons learned from a large-scale deployment,” in 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) (Bari), 4161–4168. doi: 10.1109/SMC.2019.8913928

CrossRef Full Text | Google Scholar

Koush, Y., Meskaldji, D.-E., Pichon, S., Rey, G., Rieger, S. W., Linden, D. E., et al. (2017). Learning control over emotion networks through connectivity-based neurofeedback. Cereb. Cortex 27, 1193–1202. doi: 10.1093/cercor/bhv311

PubMed Abstract | CrossRef Full Text | Google Scholar

Kovacevic, N., Ritter, P., Tays, W., Moreno, S., and McIntosh, A. R. (2015). ‘My virtual dream': collective neurofeedback in an immersive art environment. PLoS ONE 10:e0130129. doi: 10.1371/journal.pone.0130129

PubMed Abstract | CrossRef Full Text | Google Scholar

Kritikos, J., Alevizopoulos, G., and Koutsouris, D. (2021). Personalized virtual reality human-computer interaction for psychiatric and neurological illnesses: a dynamically adaptive virtual reality environment that changes according to real-time feedback from electrophysiological signal responses. Front. Hum. Neurosci. 15:596980. doi: 10.3389/fnhum.2021.596980

PubMed Abstract | CrossRef Full Text | Google Scholar

Ladouce, S., Donaldson, D. I., Dudchenko, P. A., and Ietswaart, M. (2019). Mobile EEG identifies the re-allocation of attention during real-world activity. Sci. Rep. 9, 1–10. doi: 10.1038/s41598-019-51996-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Lan, Z., Liu, Y., Sourina, O., Wang, L., Scherer, R., and Müller-Putz, G. (2020). Safe: an EEG dataset for stable affective feature selection. Adv. Eng. Inform. 44:101047. doi: 10.1016/j.aei.2020.101047

CrossRef Full Text | Google Scholar

Lee, Y.-J., Lee, G.-W., Seo, W.-S., Koo, B.-H., Kim, H.-G., and Cheon, E.-J. (2019). Neurofeedback treatment on depressive symptoms and functional recovery in treatment-resistant patients with major depressive disorder: an open-label pilot study. J. Korean Med. Sci. 34. doi: 10.3346/jkms.2019.34.e287

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, K., Jiang, Y., Gong, Y., Zhao, W., Zhao, Z., Liu, X., et al. (2019). Functional near-infrared spectroscopy-informed neurofeedback: regional-specific modulation of lateral orbitofrontal activation and cognitive flexibility. Neurophotonics 6:025011. doi: 10.1117/1.NPh.6.2.025011

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, Y., Pan, J., Wang, F., and Yu, Z. (2013). A hybrid bci system combining p300 and ssvep and its application to wheelchair control. IEEE Trans. Biomed. Eng. 60, 3156–3166. doi: 10.1109/TBME.2013.2270283

PubMed Abstract | CrossRef Full Text | Google Scholar

Linden, D. E., Habes, I., Johnston, S. J., Linden, S., Tatineni, R., Subramanian, L., et al. (2012). Real-time self-regulation of emotion networks in patients with depression. PLoS ONE 7:e38115. doi: 10.1371/journal.pone.0038115

PubMed Abstract | CrossRef Full Text | Google Scholar

Lindquist, K. A., Satpute, A. B., Wager, T. D., Weber, J., and Barrett, L. F. (2016). The brain basis of positive and negative affect: evidence from a meta-analysis of the human neuroimaging literature. Cereb. Cortex 26, 1910–1922. doi: 10.1093/cercor/bhv001

PubMed Abstract | CrossRef Full Text | Google Scholar

Lindquist, K. A., Wager, T. D., Kober, H., Bliss-Moreau, E., and Barrett, L. F. (2012). The brain basis of emotion: a meta-analytic review. Behav. Brain sci. 35:121. doi: 10.1017/S0140525X11000446

PubMed Abstract | CrossRef Full Text | Google Scholar

Linhartová, P., Látalová, A., Kóša, B., Kašpárek, T., Schmahl, C., and Paret, C. (2019). fMRI neurofeedback in emotion regulation: a literature review. NeuroImage 193, 75–92. doi: 10.1016/j.neuroimage.2019.03.011

PubMed Abstract | CrossRef Full Text | Google Scholar

Lopez-Gordo, M. A., Sanchez-Morillo, D., and Valle, F. P. (2014). Dry EEG electrodes. Sensors 14, 12847–12870. doi: 10.3390/s140712847

CrossRef Full Text | Google Scholar

Lorenzetti, V., Melo, B., Basìlio, R., Suo, C., Yücel, M., Tierra-Criollo, C. J., and Moll, J. (2018). Emotion regulation using virtual environments and real-time fMRI neurofeedback. Front. Neurol. 9:390. doi: 10.3389/fneur.2018.00390

PubMed Abstract | CrossRef Full Text | Google Scholar

Lotte, F. (2015). Signal processing approaches to minimize or suppress calibration time in oscillatory activity-based brain-computer interfaces. Proc. IEEE 103, 871–890. doi: 10.1109/JPROC.2015.2404941

CrossRef Full Text | Google Scholar

Lotte, F., Faller, J., Guger, C., Renard, Y., Pfurtscheller, G., Lécuyer, A., et al. (2012). “Combining BCI with virtual reality: towards new applications and improved BCI,” in Towards Practical Brain-Computer Interfaces, eds B. Allison, S. Dunne, R. Leeb, J. D. R. Millán, and A. Nijholt (Berlin: Springer), 197–220. doi: 10.1007/978-3-642-29746-5_10

CrossRef Full Text | Google Scholar

Maidan, I., Nieuwhof, F., Bernad-Elazari, H., Reelick, M. F., Bloem, B. R., Giladi, N., et al. (2016). The role of the frontal lobe in complex walking among patients with Parkinson's disease and healthy older adults: an fNIRS study. Neurorehabil. Neural Repair 30, 963–971. doi: 10.1177/1545968316650426

PubMed Abstract | CrossRef Full Text | Google Scholar

Maples-Keller, J. L., Bunnell, B. E., Kim, S.-J., and Rothbaum, B. O. (2017). The use of virtual reality technology in the treatment of anxiety and other psychiatric disorders. Harvard Rev. Psychiatry 25:103. doi: 10.1097/HRP.0000000000000138

PubMed Abstract | CrossRef Full Text | Google Scholar

Marx, A.-M., Ehlis, A.-C., Furdea, A., Holtmann, M., Banaschewski, T., Brandeis, D., et al. (2015). Near-infrared spectroscopy (NIRS) neurofeedback as a treatment for children with attention deficit hyperactivity disorder (ADHD)-a pilot study. Front. Hum. Neurosci. 8:1038. doi: 10.3389/fnhum.2014.01038

PubMed Abstract | CrossRef Full Text | Google Scholar

Massetti, T., Trevizan, I. L., Arab, C., Favero, F. M., Ribeiro-Papa, D. C., and de Mello Monteiro, C. B. (2016). Virtual reality in multiple sclerosis-a systematic review. Multip. Scler. Relat. Disord. 8, 107–112. doi: 10.1016/j.msard.2016.05.014

PubMed Abstract | CrossRef Full Text | Google Scholar

Mayeli, A., Misaki, M., Zotev, V., Tsuchiyagaito, A., Al Zoubi, O., Phillips, R., et al. (2020). Self-regulation of ventromedial prefrontal cortex activation using real-time fMRI neurofeedback-influence of default mode network. Hum. Brain Mapp. 41, 342–352. doi: 10.1002/hbm.24805

PubMed Abstract | CrossRef Full Text | Google Scholar

Mayeli, A., Zotev, V., Refai, H., and Bodurka, J. (2016). Real-time EEG artifact correction during fMRI using ICA. J. Neurosci. Methods 274, 27–37. doi: 10.1016/j.jneumeth.2016.09.012

PubMed Abstract | CrossRef Full Text | Google Scholar

Mehler, D. M., Sokunbi, M. O., Habes, I., Barawi, K., Subramanian, L., Range, M., et al. (2018). Targeting the affective brain-a randomized controlled trial of real-time fMRI neurofeedback in patients with depression. Neuropsychopharmacology 43, 2578–2585. doi: 10.1038/s41386-018-0126-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Melnikov, M. Y. (2021). The current evidence levels for biofeedback and neurofeedback interventions in treating depression: a narrative review. Neural Plast. 2021:8878857. doi: 10.1155/2021/8878857

PubMed Abstract | CrossRef Full Text | Google Scholar

Mennella, R., Patron, E., and Palomba, D. (2017). Frontal alpha asymmetry neurofeedback for the reduction of negative affect and anxiety. Behav. Res. Ther. 92, 32–40. doi: 10.1016/j.brat.2017.02.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Minguillon, J., Lopez-Gordo, M. A., and Pelayo, F. (2017). Trends in EEG-BCI for daily-life: requirements for artifact removal. Biomed. Signal Process. Control 31, 407–418. doi: 10.1016/j.bspc.2016.09.005

CrossRef Full Text | Google Scholar

Mirelman, A., Maidan, I., Bernad-Elazari, H., Nieuwhof, F., Reelick, M., Giladi, N., et al. (2014). Increased frontal brain activation during walking while dual tasking: an fNIRS study in healthy young adults. J. Neuroeng. Rehabil. 11, 1–7. doi: 10.1186/1743-0003-11-85

PubMed Abstract | CrossRef Full Text | Google Scholar

Mirifar, A., Beckmann, J., and Ehrlenspiel, F. (2017). Neurofeedback as supplementary training for optimizing athletes' performance: a systematic review with implications for future research. Neurosci. Biobehav. Rev. 75, 419–432. doi: 10.1016/j.neubiorev.2017.02.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Misaki, M., Phillips, R., Zotev, V., Wong, C.-K., Wurfel, B. E., Krueger, F., et al. (2018). Real-time fMRI amygdala neurofeedback positive emotional training normalized resting-state functional connectivity in combat veterans with and without PTSD: a connectome-wide investigation. NeuroImage 20, 543–555. doi: 10.1016/j.nicl.2018.08.025

PubMed Abstract | CrossRef Full Text | Google Scholar

Müller, S. M. T., Bastos, T. F., and Sarcinelli Filho, M. (2013). Proposal of a SSVEP-BCI to command a robotic wheelchair. J. Control Autom. Electr. Syst. 24, 97–105. doi: 10.1007/s40313-013-0002-9

CrossRef Full Text | Google Scholar

Müller-Putz, G., Leeb, R., Tangermann, M., Höhne, J., Kübler, A., Cincotti, F., et al. (2015). Towards noninvasive hybrid brain-computer interfaces: framework, practice, clinical application, and beyond. Proc. IEEE 103, 926–943. doi: 10.1109/JPROC.2015.2411333

CrossRef Full Text | Google Scholar

Müller-Putz, G. R., Breitwieser, C., Cincotti, F., Leeb, R., Schreuder, M., Leotta, F., et al. (2011). Tools for brain-computer interaction: a general concept for a hybrid BCI. Front. Neuroinform. 5:30. doi: 10.3389/fninf.2011.00030

PubMed Abstract | CrossRef Full Text | Google Scholar

Packheiser, J., Schmitz, J., Pan, Y., El Basbasse, Y., Friedrich, P., Güntürkün, O., et al. (2020). Using mobile EEG to investigate alpha and beta asymmetries during hand and foot use. Front. Neurosci. 14:109. doi: 10.3389/fnins.2020.00109

PubMed Abstract | CrossRef Full Text | Google Scholar

Parent, M., Albuquerque, I., Tiwari, A., Cassani, R., Gagnon, J.-F., Lafond, D., et al. (2020). Pass: a multimodal database of physical activity and stress for mobile passive body/brain-computer interface research. Front. Neurosci. 14:1274. doi: 10.3389/fnins.2020.542934

PubMed Abstract | CrossRef Full Text | Google Scholar

Peeters, F., Ronner, J., Bodar, L., van Os, J., and Lousberg, R. (2014). Validation of a neurofeedback paradigm: manipulating frontal EEG alpha-activity and its impact on mood. Int. J. Psychophysiol. 93, 116–120. doi: 10.1016/j.ijpsycho.2013.06.010

PubMed Abstract | CrossRef Full Text | Google Scholar

Pfurtscheller, G., Allison, B. Z., Bauernfeind, G., Brunner, C., Solis Escalante, T., Scherer, R., et al. (2010). The hybrid BCI. Front. Neurosci. 4:3. doi: 10.3389/fnpro.2010.00003

CrossRef Full Text | Google Scholar

Pinti, P., Aichelburg, C., Gilbert, S., Hamilton, A., Hirsch, J., Burgess, P., et al. (2018). A review on the use of wearable functional near-infrared spectroscopy in naturalistic environments. Jpn. Psychol. Res. 60, 347–373. doi: 10.1111/jpr.12206

PubMed Abstract | CrossRef Full Text | Google Scholar

Quaedflieg, C. W., Smulders, F. T., Meyer, T., Peeters, F., Merckelbach, H., and Smeets, T. (2016). The validity of individual frontal alpha asymmetry EEG neurofeedback. Soc. Cogn. Affect. Neurosci. 11, 33–43. doi: 10.1093/scan/nsv090

PubMed Abstract | CrossRef Full Text | Google Scholar

Quaresima, V., and Ferrari, M. (2019). Functional near-infrared spectroscopy (fNIRS) for assessing cerebral cortex function during human behavior in natural/social situations: a concise review. Organ. Res. Methods 22, 46–68. doi: 10.1177/1094428116658959

CrossRef Full Text | Google Scholar

Rance, M., Walsh, C., Sukhodolsky, D. G., Pittman, B., Qiu, M., Kichuk, S. A., et al. (2018). Time course of clinical change following neurofeedback. Neuroimage 181, 807–813. doi: 10.1016/j.neuroimage.2018.05.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Ravi, D., Kumar, N., and Singhi, P. (2017). Effectiveness of virtual reality rehabilitation for children and adolescents with cerebral palsy: an updated evidence-based systematic review. Physiotherapy 103, 245–258. doi: 10.1016/j.physio.2016.08.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Ray, A. M., Sitaram, R., Rana, M., Pasqualotto, E., Buyukturkoglu, K., Guan, C., et al. (2015). A subject-independent pattern-based brain-computer interface. Front. Behav. Neurosci. 9:269. doi: 10.3389/fnbeh.2015.00269

PubMed Abstract | CrossRef Full Text | Google Scholar

Renard, Y., Lotte, F., Gibert, G., Congedo, M., Maby, E., Delannoy, V., et al. (2010). Openvibe: an open-source software platform to design, test, and use brain-computer interfaces in real and virtual environments. Presence 19, 35–53. doi: 10.1162/pres.19.1.35

CrossRef Full Text | Google Scholar

Ries, A. J., Touryan, J., Vettel, J., McDowell, K., and Hairston, W. D. (2014). A comparison of electroencephalography signals acquired from conventional and mobile systems. J. Neurosci. Neuroeng. 3, 10–20. doi: 10.1166/jnsne.2014.1092

CrossRef Full Text | Google Scholar

Rohani, D. A., and Puthusserypady, S. (2015). BCI inside a virtual reality classroom: a potential training tool for attention. EPJ Nonlin. Biomed. Phys. 3, 1–14. doi: 10.1140/epjnbp/s40366-015-0027-z

CrossRef Full Text | Google Scholar

Ros, T., Enriquez-Geppert, S., Zotev, V., Young, K. D., Wood, G., Whitfield-Gabrieli, S., et al. (2020). Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-NF checklist). Brain 143, 1674–1685. doi: 10.1093/brain/awaa009

PubMed Abstract | CrossRef Full Text | Google Scholar

Rosanne, O., Albuquerque, I., Cassani, R., Gagnon, J.-F., Tremblay, S., and Falk, T. H. (2021). Adaptive filtering for improved eeg-based mental workload assessment of ambulant users. Front. Neurosci. 15:611962. doi: 10.3389/fnins.2021.611962

PubMed Abstract | CrossRef Full Text | Google Scholar

Rosenfeld, J. P., Baehr, E., Baehr, R., Gotlib, I. H., and Ranganath, C. (1996). Preliminary evidence that daily changes in frontal alpha asymmetry correlate with changes in affect in therapy sessions. Int. J. Psychophysiol. 23, 137–141. doi: 10.1016/0167-8760(96)00037-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Scheinost, D., Stoica, T., Saksa, J., Papademetris, X., Constable, R., Pittenger, C., et al. (2013). Orbitofrontal cortex neurofeedback produces lasting changes in contamination anxiety and resting-state connectivity. Transl. Psychiatry 3:e250. doi: 10.1038/tp.2013.24

PubMed Abstract | CrossRef Full Text | Google Scholar

Scheinost, D., Stoica, T., Wasylink, S., Gruner, P., Saksa, J., Pittenger, C., et al. (2014). Resting state functional connectivity predicts neurofeedback response. Front. Behav. Neurosci. 8:338. doi: 10.3389/fnbeh.2014.00338

PubMed Abstract | CrossRef Full Text | Google Scholar

Shibata, K., Lisi, G., Cortese, A., Watanabe, T., Sasaki, Y., and Kawato, M. (2019). Toward a comprehensive understanding of the neural mechanisms of decoded neurofeedback. Neuroimage 188, 539–556. doi: 10.1016/j.neuroimage.2018.12.022

PubMed Abstract | CrossRef Full Text | Google Scholar

Sitaram, R., Ros, T., Stoeckel, L., Haller, S., Scharnowski, F., Lewis-Peacock, J., et al. (2017). Closed-loop brain training: the science of neurofeedback. Nat. Rev. Neurosci. 18, 86–100. doi: 10.1038/nrn.2016.164

PubMed Abstract | CrossRef Full Text | Google Scholar

Sorger, B., Scharnowski, F., Linden, D. E., Hampson, M., and Young, K. D. (2019). Control freaks: towards optimal selection of control conditions for fmri neurofeedback studies. Neuroimage 186, 256–265. doi: 10.1016/j.neuroimage.2018.11.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Spüler, M., and Niethammer, C. (2015). Error-related potentials during continuous feedback: using EEG to detect errors of different type and severity. Front. Hum. Neurosci. 9:155. doi: 10.3389/fnhum.2015.00155

PubMed Abstract | CrossRef Full Text | Google Scholar

Strait, M., and Scheutz, M. (2014). What we can and cannot (yet) do with functional near infrared spectroscopy. Front. Neurosci. 8:117. doi: 10.3389/fnins.2014.00117

PubMed Abstract | CrossRef Full Text | Google Scholar

Strehl, U. (2014). What learning theories can teach us in designing neurofeedback treatments. Front. Hum. Neurosci. 8:894. doi: 10.3389/fnhum.2014.00894

PubMed Abstract | CrossRef Full Text | Google Scholar

Sulzer, J., Haller, S., Scharnowski, F., Weiskopf, N., Birbaumer, N., Blefari, M. L., et al. (2013). Real-time fMRI neurofeedback: progress and challenges. Neuroimage 76, 386–399. doi: 10.1016/j.neuroimage.2013.03.033

PubMed Abstract | CrossRef Full Text | Google Scholar

Tai, K., and Chau, T. (2009). Single-trial classification of NIRS signals during emotional induction tasks: towards a corporeal machine interface. J. Neuroeng. Rehabil. 6, 1–14. doi: 10.1186/1743-0003-6-39

PubMed Abstract | CrossRef Full Text | Google Scholar

Taschereau-Dumouchel, V., Cortese, A., Lau, H., and Kawato, M. (2020). Conducting decoded neurofeedback studies. Soc. Cogn. Affect. Neurosci. 2020, nsaa063. doi: 10.1093/scan/nsaa063

CrossRef Full Text | Google Scholar

Theeuwes, J., Kramer, A. F., Hahn, S., and Irwin, D. E. (1998). Our eyes do not always go where we want them to go: capture of the eyes by new objects. Psychol. Sci. 9, 379–385. doi: 10.1111/1467-9280.00071

CrossRef Full Text | Google Scholar

Thibault, R. T., Lifshitz, M., and Raz, A. (2017). Neurofeedback or neuroplacebo? Brain 140, 862–864. doi: 10.1093/brain/awx033

CrossRef Full Text | Google Scholar

Thibault, R. T., MacPherson, A., Lifshitz, M., Roth, R. R., and Raz, A. (2018). Neurofeedback with fMRI: a critical systematic review. Neuroimage 172, 786–807. doi: 10.1016/j.neuroimage.2017.12.071

PubMed Abstract | CrossRef Full Text | Google Scholar

Thibault, R. T., and Raz, A. (2017). The psychology of neurofeedback: Clinical intervention even if applied placebo. Am. Psychol. 72:679. doi: 10.1037/amp0000118

PubMed Abstract | CrossRef Full Text | Google Scholar

Thibodeau, R., Jorgensen, R. S., and Kim, S. (2006). Depression, anxiety, and resting frontal EEG asymmetry: a meta-analytic review. J. Abnorm. Psychol. 115:715. doi: 10.1037/0021-843X.115.4.715

PubMed Abstract | CrossRef Full Text | Google Scholar

Trambaiolli, L. R., Biazoli, C. E., Cravo, A. M., Falk, T. H., and Sato, J. R. (2018a). Functional near-infrared spectroscopy-based affective neurofeedback: feedback effect, illiteracy phenomena, and whole-connectivity profiles. Neurophotonics 5:035009. doi: 10.1117/1.NPh.5.3.035009

PubMed Abstract | CrossRef Full Text | Google Scholar

Trambaiolli, L. R., Biazoli, C. E., Cravo, A. M., and Sato, J. R. (2018b). Predicting affective valence using cortical hemodynamic signals. Sci. Rep. 8, 1–12. doi: 10.1038/s41598-018-23747-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Trambaiolli, L. R., Kohl, S. H., Linden, D. E., and Mehler, D. M. (2021a). Neurofeedback training in major depressive disorder: a systematic review of clinical efficacy, study quality and reporting practices. Neurosci. Biobehav. Rev. 125, 33–56. doi: 10.1016/j.neubiorev.2021.02.015

PubMed Abstract | CrossRef Full Text | Google Scholar

Trambaiolli, L. R., Tossato, J., Cravo, A. M., Biazoli, C. E. Jr, and Sato, J. R. (2021b). Subject-independent decoding of affective states using functional near-infrared spectroscopy. PLoS ONE 16:e0244840. doi: 10.1371/journal.pone.0244840

PubMed Abstract | CrossRef Full Text | Google Scholar

Tursic, A., Eck, J., Lührs, M., Linden, D. E., and Goebel, R. (2020). A systematic review of fMRI neurofeedback reporting and effects in clinical populations. NeuroImage 2020:102496. doi: 10.1016/j.nicl.2020.102496

PubMed Abstract | CrossRef Full Text | Google Scholar

Val-Calvo, M., Álvarez-Sánchez, J. R., Ferrández-Vicente, J. M., and Fernández, E. (2019). Optimization of real-time EEG artifact removal and emotion estimation for human-robot interaction applications. Front. Comput. Neurosci. 13:80. doi: 10.3389/fncom.2019.00080

PubMed Abstract | CrossRef Full Text | Google Scholar

Vanzella, P., Balardin, J. B., Furucho, R. A., Zimeo Morais, G. A., Braun Janzen, T., Sammler, D., et al. (2019). fNIRS responses in professional violinists while playing duets: evidence for distinct leader and follower roles at the brain level. Front. Psychol. 10:164. doi: 10.3389/fpsyg.2019.00164

PubMed Abstract | CrossRef Full Text | Google Scholar

von Lühmann, A., Wabnitz, H., Sander, T., and Müller, K.-R. (2016). M3BA: a mobile, modular, multimodal biosignal acquisition architecture for miniaturized EEG-NIRS-based hybrid BCI and monitoring. IEEE Trans. Biomed. Eng. 64, 1199–1210. doi: 10.1109/TBME.2016.2594127

PubMed Abstract | CrossRef Full Text | Google Scholar

Wald, L. L., McDaniel, P. C., Witzel, T., Stockmann, J. P., and Cooley, C. Z. (2020). Low-cost and portable MRI. J. Magn. Reson. Imaging 52, 686–696. doi: 10.1002/jmri.26942

CrossRef Full Text | Google Scholar

Wan, F., Nan, W., Vai, M. I., and Rosa, A. (2014). Resting alpha activity predicts learning ability in alpha neurofeedback. Front. Hum. Neurosci. 8:500. doi: 10.3389/fnhum.2014.00500

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, S.-Y., Lin, I.-M., Fan, S.-Y., Tsai, Y.-C., Yen, C.-F., Yeh, Y.-C., et al. (2019). The effects of alpha asymmetry and high-beta down-training neurofeedback for patients with the major depressive disorder and anxiety symptoms. J. Affect. Disord. 257, 287–296. doi: 10.1016/j.jad.2019.07.026

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, Y.-T., Wang, Y., Cheng, C.-K., and Jung, T.-P. (2013). “Developing stimulus presentation on mobile devices for a truly portable SSVEP-based BCI,” in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (Osaka), 5271–5274.

PubMed Abstract | Google Scholar

Watanabe, T., Sasaki, Y., Shibata, K., and Kawato, M. (2017). Advances in fMRI real-time neurofeedback. Trends Cogn. Sci. 21, 997–1010. doi: 10.1016/j.tics.2017.09.010

CrossRef Full Text | Google Scholar

Weber, E., Köberl, A., Frank, S., and Doppelmayr, M. (2011). Predicting successful learning of smr neurofeedback in healthy participants: methodological considerations. Appl. Psychophysiol. Biofeedb. 36, 37–45. doi: 10.1007/s10484-010-9142-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Weech, S., Kenny, S., and Barnett-Cowan, M. (2019). Presence and cybersickness in virtual reality are negatively related: a review. Front. Psychol. 10:158. doi: 10.3389/fpsyg.2019.00158

PubMed Abstract | CrossRef Full Text | Google Scholar

Yamin, H. G., Gazit, T., Tchemodanov, N., Raz, G., Jackont, G., Charles, F., et al. (2017). Depth electrode neurofeedback with a virtual reality interface. Brain Comput. Interfaces 4, 201–213. doi: 10.1080/2326263X.2017.1338008

CrossRef Full Text | Google Scholar

Young, K. D., Siegle, G. J., Misaki, M., Zotev, V., Phillips, R., Drevets, W. C., et al. (2018). Altered task-based and resting-state amygdala functional connectivity following real-time fMRI amygdala neurofeedback training in major depressive disorder. NeuroImage 17, 691–703. doi: 10.1016/j.nicl.2017.12.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Young, K. D., Siegle, G. J., Zotev, V., Phillips, R., Misaki, M., Yuan, H., et al. (2017). Randomized clinical trial of real-time fMRI amygdala neurofeedback for major depressive disorder: effects on symptoms and autobiographical memory recall. Am. J. Psychiatry 174, 748–755. doi: 10.1176/appi.ajp.2017.16060637

PubMed Abstract | CrossRef Full Text | Google Scholar

Young, K. D., Zotev, V., Phillips, R., Misaki, M., Yuan, H., Drevets, W. C., et al. (2014). Real-time fMRI neurofeedback training of amygdala activity in patients with major depressive disorder. PLoS ONE 9:e88785. doi: 10.1371/journal.pone.0088785

CrossRef Full Text | Google Scholar

Yuan, H., Young, K. D., Phillips, R., Zotev, V., Misaki, M., and Bodurka, J. (2014). Resting-state functional connectivity modulation and sustained changes after real-time functional magnetic resonance imaging neurofeedback training in depression. Brain Connect. 4, 690–701. doi: 10.1089/brain.2014.0262

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, R., Xiao, W., Ding, Y., Feng, Y., Peng, X., Shen, L., et al. (2020). Recording brain activities in unshielded earth92s field with optically pumped atomic magnetometers. Sci. Adv. 6:eaba8792. doi: 10.1126/sciadv.aba8792

PubMed Abstract | CrossRef Full Text | Google Scholar

Zotev, V., and Bodurka, J. (2020). Effects of simultaneous real-time fMRI and EEG neurofeedback in major depressive disorder evaluated with brain electromagnetic tomography. NeuroImage 28:102459. doi: 10.1016/j.nicl.2020.102459

PubMed Abstract | CrossRef Full Text | Google Scholar

Zotev, V., Krueger, F., Phillips, R., Alvarez, R. P., Simmons, W. K., Bellgowan, P., et al. (2011). Self-regulation of amygdala activation using real-time fMRI neurofeedback. PLoS ONE 6:e24522. doi: 10.1371/journal.pone.0024522

PubMed Abstract | CrossRef Full Text | Google Scholar

Zotev, V., Mayeli, A., Misaki, M., and Bodurka, J. (2020). Emotion self-regulation training in major depressive disorder using simultaneous real-time fMRI and EEG neurofeedback. NeuroImage 27:102331. doi: 10.1016/j.nicl.2020.102331

PubMed Abstract | CrossRef Full Text | Google Scholar

Zotev, V., Phillips, R., Misaki, M., Wong, C. K., Wurfel, B. E., Krueger, F., et al. (2018). Real-time fMRI neurofeedback training of the amygdala activity with simultaneous EEG in veterans with combat-related PTSD. NeuroImage 19, 106–121. doi: 10.1016/j.nicl.2018.04.010

PubMed Abstract | CrossRef Full Text | Google Scholar

Zotev, V., Phillips, R., Young, K. D., Drevets, W. C., and Bodurka, J. (2013). Prefrontal control of the amygdala during real-time fMRI neurofeedback training of emotion regulation. PLoS ONE 8:e79184. doi: 10.1371/journal.pone.0079184

PubMed Abstract | CrossRef Full Text | Google Scholar

Zotev, V., Phillips, R., Yuan, H., Misaki, M., and Bodurka, J. (2014). Self-regulation of human brain activity using simultaneous real-time fMRI and EEG neurofeedback. NeuroImage 85, 985–995. doi: 10.1016/j.neuroimage.2013.04.126

PubMed Abstract | CrossRef Full Text | Google Scholar

Zotev, V., Yuan, H., Misaki, M., Phillips, R., Young, K. D., Feldner, M. T., et al. (2016). Correlation between amygdala bold activity and frontal EEG asymmetry during real-time fMRI neurofeedback training in patients with depression. NeuroImage 11, 224–238. doi: 10.1016/j.nicl.2016.02.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: neurofeedback, emotion regulation, virtual reality, naturalistic, in-the-wild, brain-computer interfaces

Citation: Trambaiolli LR, Tiwari A and Falk TH (2021) Affective Neurofeedback Under Naturalistic Conditions: A Mini-Review of Current Achievements and Open Challenges. Front. Neuroergon. 2:678981. doi: 10.3389/fnrgo.2021.678981

Received: 10 March 2021; Accepted: 28 April 2021;
Published: 24 May 2021.

Edited by:

Sylvain Delplanque, Université de Genève, Switzerland

Reviewed by:

Ahmad Mayeli, University of Pittsburgh, United States
Arianna Trettel, BrainSigns, Italy

Copyright © 2021 Trambaiolli, Tiwari and Falk. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Lucas R. Trambaiolli, ltrambaiolli@mclean.harvard.edu