Edited by: Fabien Lotte, Institut National de Recherche en Informatique et en Automatique (INRIA), France
Reviewed by: Quentin Noirhomme, Maastricht University, Netherlands; Dennis J. McFarland, Wadsworth Center, United States; Jérémie Mattout, Lyon Neuroscience Research Center, France
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 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.
Our objective was to investigate the capacity to control a P3-based brain-computer interface (BCI) device for communication and its related (temporal) attention processing in a sample of amyotrophic lateral sclerosis (ALS) patients with respect to healthy subjects. The ultimate goal was to corroborate the role of cognitive mechanisms in event-related potential (ERP)-based BCI control in ALS patients. Furthermore, the possible differences in such attentional mechanisms between the two groups were investigated in order to unveil possible alterations associated with the ALS condition. Thirteen ALS patients and 13 healthy volunteers matched for age and years of education underwent a P3-speller BCI task and a rapid serial visual presentation (RSVP) task. The RSVP task was performed by participants in order to screen their temporal pattern of attentional resource allocation, namely: (i) the temporal attentional filtering capacity (scored as T1%); and (ii) the capability to adequately update the attentive filter in the temporal dynamics of the attentional selection (scored as T2%). For the P3-speller BCI task, the online accuracy and information transfer rate (ITR) were obtained. Centroid Latency and Mean Amplitude of N200 and P300 were also obtained. No significant differences emerged between ALS patients and Controls with regards to online accuracy (
The non-invasive brain-computer interface (BCI) based on the visual event-related potential (ERP) known as P300 (P3; Farwell and Donchin,
Within the range of users in need of a BCI for communication and control, those with amyotrophic lateral sclerosis (ALS) represent
A number of studies reported that ALS patients can communicate by using a P3-based BCI (Marchetti et al.,
In this regard, it is important to note that cognitive deficits have been described in ALS patients (Lomen-Hoerth et al.,
In the present study, we investigated whether the accuracy in mastering a P3-based BCI by an ALS population sample would be affected by the previously identified alterations in the attention processing and whether these alterations would be exclusive of the ALS population. To this purpose, we compared a group of ALS patients with a group of healthy volunteers both controlling a P3-speller (Farwell and Donchin,
Thirteen participants (8 males; mean age 62.2 ± 13; years of formal education 13.7 ± 5.1) with ALS diagnosis (ALS group) and 13 age and years of education-matched participants (9 males; mean age 55.3 ± 9; years of formal education 13.3 ± 3) with no history of neurological/psychiatric disorders (Control group) were enrolled in the study. Seven out of 13 ALS patients participated in the previous study (Riccio et al.,
The ALS patients were recruited through the ALS Center of the Policlinico “Umberto I”, Sapienza University, Rome. The study was conducted at Fondazione Santa Lucia, IRCCS, Rome and approved by the Independent Ethics Committee of Fondazione Santa Lucia. All participants (or the legal representatives of ALS patients when required) provided a written informed consent.
The inclusion criterion for the ALS patients was the ability (also with the help of an AT device if required) to clearly communicate (at least) a binary response (yes/no). Patients with other concomitant neurological or psychiatric disorders, any impediment in the acquisition of electroencephalography (EEG) data from the scalp (e.g., wounds, dermatitis), severe concomitant pathologies (fever, infections, metabolic disorders, severe heart failure), or episodes of reflex epilepsy were excluded from the study.
The level of physical disability was assessed by means of the “ALS Functional Rating Scale-Revised” (ALSFRS-R; Cedarbaum et al.,
Participants’ characteristics.
ALS group | Control group | |
---|---|---|
Age (years) | 62.2 ± 13 (40–80) | 55.3 ± 9 (44–68) |
Sex (M/F) | 8/5 | 9/4 |
Years of formal education | 13.7 ± 5.1 (5–18) | 13.4 ± 3.4 (8–18) |
EF (impaired/not impaired) | 5/6 | 5/8 |
EF (perseverative responses) | 88.5 ± 16.5 | 96.6 ± 20.2 |
EF (total errors) | 88.5 ± 14.3 | 93.6 ± 15.4 |
SA (impaired/not impaired) | 3/7 | 1/12 |
SA (errors) | 2.4 ± 2.9 | 0.8 ± 1.9 |
WM (impaired/not impaired) | 4/5 | 1/10 |
WM (omissions) | 3.8 ± 3.8 | 1.6 ± 2.0 |
ALSFRS-R | 31.2 ± 10.4 (12–41) | - |
Onset (S/B) | 5/8 | - |
Time since diagnosis (mo) | 26.8 ± 22.6 | - |
Participants underwent a cognitive assessment focused on attention domains, in order to have individual baseline profiles. Two clinical neuropsychological tests were applied for the cognitive screening. The computerized test for attentional performance (TAP; Zimmermann and Fimm,
The experimental design consisted of two separate sessions (performed on two different days): the BCI session and the psychological session (see below for details).
Scalp potentials were acquired by means of a 16-channel amplifier (g.MOBILAB, g.tec, Austria) from eight active electrodes (g.Ladybird, g.tec, Austria) placed according to 10–10 international standard (Fz, Cz, Pz, Oz, P3, P4, PO7, and PO8; right ear lobe reference, left mastoid ground). This experimental choice was dictated by a reasonable trade-off between a not exhausting experimental procedure for ALS patients and a widely accepted eight electrodes configuration to ensure a P300 based-BCI successful control. Signals were digitized at 256 Hz. Stimulus paradigm and online delivery were managed by means of the BCI2000 framework (Schalk et al.,
During the calibration phase (i.e., no feedback on performance), the subjects had to focus on 15 items forming three predefined words (3 runs; 5 items for each run). The target to focus on was shown to the participants by a single flash, after which rows and columns were randomly intensified for 125 ms, with an inter stimulus interval (ISI) of 125 ms. Participants were suggested to mentally count how many times that target was flashing. Calibration data were segmented into epochs lasting 800 ms (time 0 marked the stimulus onset) that were fed into a stepwise linear discriminant analysis (SWLDA) to determine the classifier coefficients (Krusienski et al.,
The individual temporal pattern of attentional resource allocation was tested by means of a rapid serial visual presentation (RSVP) paradigm which corresponds to an attentional blink (AB) paradigm in Kranczioch et al. (
Upon the stimulus stream delivery, participants were asked to answer the following questions: (1) whether the green letter (T1) was a vowel (T1 was a vowel on 50% of the trials); and (2) whether the black X (T2) was contained in the stimulus stream. In the case of ALS patients, the answers to the questions were given according to their residual motor activity (e.g., verbal response, head movements, eye movements).
Twenty practice trials preceded a total of 160 experimental trials (32 trials for each of the five T2 conditions); these latter were fully randomized within two presentation blocks separated by a pause of 5 min.
All acquired data were preprocessed as follows. High and low pass filters (4th order Butterworth filter) were applied with a cut off frequency of 1 Hz and 20 Hz, respectively. EEG signals with peak amplitude higher than 70 μV or lower than −70 μV were removed. Data were then segmented into epochs (time 0 denoted the stimulus onset) lasting 800 ms and 1000 ms for the BCI and ERPs analysis, respectively. Both target and non-target stimulus-related epochs were considered.
The BCI online accuracy was expressed as the percentage of correct selections (i.e., the ratio between the number of correct selections and the total number of selections). Furthermore, an offline estimation of both the accuracy and the information transfer rate (ITR, Wolpaw et al.,
To estimate the offline accuracy, a baseline correction was performed based on the mean amplitude of signal within the 200 ms pre-stimulus interval. The offline accuracy was then calculated for each stimulation sequence by means of a 7-fold cross-validation technique according to which six runs were used as training dataset to extract SWLDA classifier parameters and one run was used as testing dataset. A mean accuracy value for each stimulation sequence was obtained by averaging the values resulting from the seven iterations.
The ITR (bits/min) was estimated for each subject and each stimulation sequence based on the definition of bit-rate as in Wolpaw et al. (
The relative contribution of the N2 and P3 ERP components to the BCI accuracy was investigated offline as follows. Differences in the amplitudes of ERPs that were elicited by the stimulus types (target vs. non-target) were quantified using the coefficient of determination R2. We considered the epochs relative to all seven runs. The R2 values range from 0 to 1, wherein higher values correspond to larger explained variances. A
As for the ITR, its values were computed by segmenting data into epochs between 0 ms and 550 ms after the stimulus onset—ITR (0–550 ms)—to ensure that the temporal interval would include both N2 and P3 ERP components.
We focused the ERP analysis on the N2 as the earliest ERP that reliably correlates with visual awareness (Visual Awareness Negativity; Railo et al.,
In this offline ERP analysis, the epochs in which a target stimulus occurred within the 500 ms preceding the stimulus onset were removed in order to reduce the contamination between consecutive epochs and the ERP overlapping (Treder and Blankertz,
The mean of both the N2 and P3 amplitude and the centroid latency (Luck,
As for the RSVP data set, the accuracy of T1 and T2 detection (T1%; T2%) was estimated (T2% was considered only in trials in which T1 had been correctly identified). T1% was considered an index of participants’ temporal attentional filtering capacity and T2% was considered as an index of the capability to adequately update the attentive filter (Riccio et al.,
Between-group (ALS and Control) differences in terms of BCI control performance were evaluated as follows. A (non-parametric) Mann-Whitney U test was applied to assess the between-group difference in BCI online accuracy (accuracy scores not normally distributed). A Student’s
The (non-parametric) Spearman’s rank order correlation was applied to investigate the possible correlation between ALSFRS-R scores (not normally distributed) and the ITR values.
To investigate whether ALS patients showed differences with respect to Control group in attention processing during the BCI task, we conducted two MANOVAs to determine the effect of group (independent variable) on both P3-MA and P3-CL (dependent variables). The same analysis (two MANOVAs) was performed to determine the effect of group on both N2-MA and N2-CL.
To investigate whether the temporal pattern of attentional resource allocation would be correlated with the BCI performance level (ITR), we performed a correlation analysis (Pearson’s correlation coefficients) between the ITR (0–800 ms), the P3-MA in Pz and T1% and T2%. Such correlation was sought either by pooling all data from ALS and Control groups and by considering only ALS group data.
The existence of alterations in attentional resources allocation in the ALS group was assessed by means of a MANOVA to test the effect of group (independent variable) on T1% and T2% (dependent variables).
The ALS and Control groups did not show significant differences as regard demographic characteristics (Student’s
We did not find a significant between-group difference in the online accuracy (Figure
Box plots illustrate the comparison between groups relative to the online performance
The linear regression analysis (
Scatter plots illustrate the relationship of the ITR (0–550 ms) with the N2Rsquare (blue dots) and the P3Rsquare (red dots) in the ALS (experimental group)
We found that the ITR and ALSFRS-R scores showed a high tendency to correlate which did not reach a significance (
No significant differences were found between ALS and Control groups (MANOVAs) in P3-MA (
As illustrated in Figure
P300 topography and waveforms in ALS (Experimental Group) and Control group. Traces in the middle panel represent the grand average of the difference between target and non-target electroencephalography (EEG) amplitude as a function of time (interval between 0 = stimulus onset and 1000 ms) recorded for ALS (
The analysis of the relation between cognitive substrates and BCI performance as measured by means of RSVP and BCI data returned a significant positive correlation between T1% and the ITR (
The MANOVA (
This study aimed at investigating whether ALS patients showed differences in the ability to control a P3-speller BCI system with respect to healthy subjects. We focused on the attention processing involved in the delivering of the visual BCI stimulation paradigm, in order to further (Riccio et al.,
First, we found that the ALS patients showed a significantly lower ITR in the P3-speller BCI task with respect to Controls whereas the online performance was comparable between the two groups.
This finding is not in line with what reported by McCane et al. (
In addition to this, the overall methods (and metrics) to estimate the P3-based BCI performance are not directly comparable between the two studies. We “only” found the (offline) ITR as a distinctive metric of the ability to use a P3-based BCI in ALS with respect to Control group.
In the P3-speller task, the act of focusing attention on the target letter modulates the visual processing of the stimulus. Our ERP findings indicate that the P3 mean latency was significantly higher in ALS with respect to control group while no difference was found in the N2 parameters between the two groups. The finding of a delayed P3 associated with a “normal” N2 (i.e., physiological stimulus categorization process) in ALS can be interpreted as a
The P3 wave component showed a frontal topography in the ALS group as compared to the parietal distribution observed in the Control group (Figure
Fabiani and Friedman (
We found that the N2-R2 and not the P3-R2 coefficient significantly predicted the BCI accuracy only in ALS group, accounting for the 59% of the ITR variance. Based on the assumption that such coefficients mostly returned the contribution of N2 and of P3 waves to the BCI classification performance, these findings indicate that in ALS patients the N2 elicited during the P3-speller task would have a major role with respect to P3 in successful target selection.
The presence of a jitter in the P3 latency has been described in healthy subjects controlling a P3-based BCI system and its magnitude would be correlated with the online performance (Thompson et al.,
According to our previous findings (Riccio et al.,
In the present study, we confirmed that the temporal filter in attention processing of visual stimuli in ALS patients was altered, by directly compare the T1% and T2% values obtained from ALS and Control group. Specifically, we found that the capability to detect T1 (but not T2) was lower in the ALS group.
As such, this finding is consistent with that of a delay in the P3 latency which reflects a deficit in the temporal aspect (i.e., post-perceptual stage of the stimulus attentional processing) of the context update. Taken altogether, these findings clearly indicate the existence of an alteration in the temporal aspects of the visual stimulus processing as presented in a “conventional” P3-speller matrix in ALS population and that this time-related alteration in the capacity to temporally process visual stimuli does influence the rate of success in BCI control.
Our findings might lay the groundwork for future clarification of some of the relevant issues in the actual deployment of ERPs-based BCI for communication to ALS users, such as the impact of end-users’ cognitive profile in designing user-centered (Liberati et al.,
Some limitations pertaining different aspects of this study deserve to be mentioned. First, our ALS population does not include ALS patients in a complete locked-in state (LIS). Although this restriction in the inclusion criterion was mandatory to allow the cognitive screening, it prevents any generalization of our findings to those patients with no means of communication (i.e., complete LIS). In this framework, our study suggests a possible role of the cognitive assessment to be performed in ALS patients before they would be in a LIS condition, including the specific cognitive abilities identified here as critical for P3-based BCI usage.
Second, our findings relative to BCI factors influencing BCI performance allow us to make inferences only regarding the control of a P3-speller in a group of ALS patients and cannot be generalized to the control of other BCIs. It is conceivable that when exploiting different features to control other BCIs, temporal aspects of attention would not have a comparable role. An example of “alternative” features to P3 would be the N400 wave, involved in the elaboration of meaningful stimuli such as face recognition (Kaufmann et al.,
Third, attention is a complex domain of the cognitive functions (Posner,
This study involved a group of participants with ALS and a group of healthy participants matched for age and years of formal education. Our results showed that both the capacity to accomplish the P3-speller task and the timing of the allocation of attentional resources in the post-perceptual stage of stimulus processing were altered in ALS patients. Furthermore, we confirmed that the capacity to temporally filter a target stimulus within a stream of stimuli was related to a lower capacity for ALS to control a P3-speller.
Developing AT devices that restore communication in people with severe motor disabilities is a central issue of BCI research (Millán et al.,
AR was responsible for experimental design, data collection, analysis of data and manuscript writing. FS was responsible for data acquisition and analysis. LS was responsible for behavioral assessment (attention tasks). AP and MI were responsible for the patients’ enrolment. MO-B, DM and FC supervised the overall experimental design implementation, data interpretation and manuscript editing.
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.