Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms

Abstract

Brain-computer interfaces (BCIs) have been explored in the field of neuroengineering to investigate how the brain can use these systems to control external devices. We review the principles and approaches we have taken to develop a sensorimotor rhythm electroencephalography (EEG)-based brain-computer interface (BCI). The methods include developing BCI systems incorporating the control of physical devices to increase user engagement, improving BCI systems by inversely mapping scalp-recorded EEG signals to the cortical source domain, integrating BCI with noninvasive neuromodulation strategies to improve learning, and incorporating mind-body awareness training to enhance BCI learning and performance. The challenges and merits of these strategies are discussed, together with recent findings. Our work indicates that the sensorimotor-rhythm-based noninvasive BCI has the potential to provide communication and control capabilities as an alternative to physiological motor pathways.

Real and imaginary motion classification based on rough set analysis of EEG signals for multimedia applications

“…Every signal is decomposed into the time-frequency domain (TF): it is split into frequency bands, following the standard EEG ranges: delta (2-4 Hz), theta (4-7 Hz), alpha (8-15 Hz), beta (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29), and gamma (30-59 Hz). The next step is extraction of filtered signals envelopes using Hilbert transform [29], being the indication of the overall activity in the particular frequency band.…”

Motor Imagery EEG Signal Processing and Classification using Machine Learning Approach

“…Out of these potentials, SMR-based BCI provides a high degree of freedom in association with real and imaginary movements of hands, arms, feet and tongue [8]. The neural activities associated with SMRbased motor imagery (MI) BCI are the so-called mu (7)(8)(9)(10)(11)(12)(13) and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) Hz) rhythms [9]. These rhythms are readily measurable in both healthy and disabled people with neuromuscular injuries.…”

A Review on Bio-Signal Processing to identify Cognitive States from EEG Signal

“…Bin He et al (2015) review the concepts to progress a sensorimotor rhythm EEG grounded on brain-computer interface and then describe the method which comprise of emerging BCI systems combining the control of physical devices to escalate user engagement, make better BCI systems by inversely mapping scalp -recorded EEG signals to the cortical source domain, for improvement of learning method mix BCI with concept of noninvasive neuromodulation , further to improve BCI learning & results they incorporate mind-body cognizance training. The result shows that rhythm-based sensorimotor-noninvasive BCI is efficient in giving communication and control competences.…”