Brain-Computer Interface (BCI) is a promising technique for establishing a direct link between the human brain and an external computerized device bypassing the normal pathway when it is not functional due to any brain/spinal cord related injury or diseases. BCI allows severely disabled persons to communicate with the outside world by controlling certain computerized devices such as a computer, wheelchair, neural prosthetics, etc. In addition to that, it is also used as a rehabilitation tool for stroke patients and people with similar needs. Although there are different brain recording techniques (starting from non-invasive to invasive ones) to measure the electrical activity of the brain to process and use in BCI applications, scalp EEG is the most popular among them in BCI research due to its mainly non-invasive nature with other attractive features such as fine temporal resolution, simple to use, portability and lower cost.
However, scalp EEG is very much prone to undesirable artifacts that come from the non-cerebral origin. The artifacts often severely contaminate the EEG recordings and modify the shape of a particular neurological event that drives the BCI system affecting the accuracy of the BCI performance. For example, artifacts can mistakenly cause an unintentional control of the BCI device. Therefore, handling of such offending artifacts is critical in BCI research for satisfactory performance and consequently, many techniques have been developed to get rid of artifacts that create misinterpretation in the signal analysis.
Recent advances in signal processing techniques have now allowed researchers to design automatic artifact identification and removal algorithms to be able to implement online for real-world BCI applications. In addition, EEG based BCI systems have limited degree of freedom because of the noninvasive nature of EEG (lower signal resolution and mostly low frequency brain rhythms) compared to invasive brain recordings that allow to record single-neural activities or neural action potentials (neural spikes). Most BCI applications require wireless and lower number of recording channels for EEG headsets for easy mobility of users. This also limits the quality of
the signals for converting to BCI commands. In addition, higher mobility of users will introduce so many motion artifacts due to subject/headset movements and thus makes it challenging to
produce and process good quality EEG recordings for application in real-time BCI systems.
This aim of this Research Topic is to bring together original research and review articles that focus
on the recent advancement in EEG acquisition techniques, signal processing, analysis and
classification techniques for different BCI applications (e.g. MI, SSVEP, P300, etc.) with their
reported performance and limitations. In addition, future challenges and recommendations for
EEG-based BCI applications will be discussed as part of this collection.
Potential topics include but are not limited to the following:
1) Advances in EEG Recording Technology for BCI Applications
2) Advances in EEG Signal Processing and Analysis techniques for BCI Applications
3) Advances in EEG Signal Classification Techniques for Translation to BCI Commands
4) Application of Deep Learning in EEG based BCI
5) EEG Based BCI for Motor Function Recovery and Rehabilitation
6) EEG Based BCI for neural prosthesis
7) Application of different types of BCI: SSVEP, MI, ERP, P300, N100, etc. (Each type can be
a chapter)
8) Current Challenges and Future Potentials in Non-invasive BCIs
9) Advances in EEG based BCIs in Controlling computers and other devices (wheelchair, home
appliances, etc.) as a replacement of motor functions
10) Application of EEG Based BCIs in Entertainment industries (e.g. Smart TV, Gaming, etc
Brain-Computer Interface (BCI) is a promising technique for establishing a direct link between the human brain and an external computerized device bypassing the normal pathway when it is not functional due to any brain/spinal cord related injury or diseases. BCI allows severely disabled persons to communicate with the outside world by controlling certain computerized devices such as a computer, wheelchair, neural prosthetics, etc. In addition to that, it is also used as a rehabilitation tool for stroke patients and people with similar needs. Although there are different brain recording techniques (starting from non-invasive to invasive ones) to measure the electrical activity of the brain to process and use in BCI applications, scalp EEG is the most popular among them in BCI research due to its mainly non-invasive nature with other attractive features such as fine temporal resolution, simple to use, portability and lower cost.
However, scalp EEG is very much prone to undesirable artifacts that come from the non-cerebral origin. The artifacts often severely contaminate the EEG recordings and modify the shape of a particular neurological event that drives the BCI system affecting the accuracy of the BCI performance. For example, artifacts can mistakenly cause an unintentional control of the BCI device. Therefore, handling of such offending artifacts is critical in BCI research for satisfactory performance and consequently, many techniques have been developed to get rid of artifacts that create misinterpretation in the signal analysis.
Recent advances in signal processing techniques have now allowed researchers to design automatic artifact identification and removal algorithms to be able to implement online for real-world BCI applications. In addition, EEG based BCI systems have limited degree of freedom because of the noninvasive nature of EEG (lower signal resolution and mostly low frequency brain rhythms) compared to invasive brain recordings that allow to record single-neural activities or neural action potentials (neural spikes). Most BCI applications require wireless and lower number of recording channels for EEG headsets for easy mobility of users. This also limits the quality of
the signals for converting to BCI commands. In addition, higher mobility of users will introduce so many motion artifacts due to subject/headset movements and thus makes it challenging to
produce and process good quality EEG recordings for application in real-time BCI systems.
This aim of this Research Topic is to bring together original research and review articles that focus
on the recent advancement in EEG acquisition techniques, signal processing, analysis and
classification techniques for different BCI applications (e.g. MI, SSVEP, P300, etc.) with their
reported performance and limitations. In addition, future challenges and recommendations for
EEG-based BCI applications will be discussed as part of this collection.
Potential topics include but are not limited to the following:
1) Advances in EEG Recording Technology for BCI Applications
2) Advances in EEG Signal Processing and Analysis techniques for BCI Applications
3) Advances in EEG Signal Classification Techniques for Translation to BCI Commands
4) Application of Deep Learning in EEG based BCI
5) EEG Based BCI for Motor Function Recovery and Rehabilitation
6) EEG Based BCI for neural prosthesis
7) Application of different types of BCI: SSVEP, MI, ERP, P300, N100, etc. (Each type can be
a chapter)
8) Current Challenges and Future Potentials in Non-invasive BCIs
9) Advances in EEG based BCIs in Controlling computers and other devices (wheelchair, home
appliances, etc.) as a replacement of motor functions
10) Application of EEG Based BCIs in Entertainment industries (e.g. Smart TV, Gaming, etc
