OPTICAL+: a frequency-based deep learning scheme for recognizing brain wave signals

• Published in: PeerJ. Computer science Vol. 7; p. e375
• Main Authors: Kumar, Shiu, Sharma, Ronesh, Sharma, Alok
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 04.02.2021; PeerJ, Inc; PeerJ Inc
• Subjects: Adaptive algorithms; Adaptive filters; Adaptive systems; Algorithms; Artificial Intelligence; Biometrics; Brain; Brain wave; Brain-Computer Interface; Cable television broadcasting industry; Classification; Common spatial pattern (CSP); Data compression; Deep learning; Electroencephalography; Feature selection; Frequencies; Genetic algorithms; Human-Computer Interaction; Human-computer interaction (HCI); Hybrid systems; Informative frequency band (IFB); Long short-term memory (LSTM); Machine learning; Motor imagery (MI); Noise; Seizures; Sensors; Sleep; Support vector machines; United States; Motor imagery (MI); Informative frequency band (IFB); Brain wave; Common spatial pattern (CSP); Long short-term memory (LSTM); Human-computer interaction (HCI)
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.375

A human–computer interaction (HCI) system can be used to detect different categories of the brain wave signals that can be beneficial for neurorehabilitation, seizure detection and sleep stage classification. Research on developing HCI systems using brain wave signals has progressed a lot over the years. However, real-time implementation, computational complexity and accuracy are still a concern. In this work, we address the problem of selecting the appropriate filtering frequency band while also achieving a good system performance by proposing a frequency-based approach using long short-term memory network (LSTM) for recognizing different brain wave signals. Adaptive filtering using genetic algorithm is incorporated for a hybrid system utilizing common spatial pattern and LSTM network. The proposed method (OPTICAL+) achieved an overall average classification error rate of 30.41% and a kappa coefficient value of 0.398, outperforming the state-of-the-art methods. The proposed OPTICAL+ predictor can be used to develop improved HCI systems that will aid in neurorehabilitation and may also be beneficial for sleep stage classification and seizure detection.