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

PeerJ Computer Science

Alok Sharma¹  Shiu Kumar²  Ronesh Sharma² 

[1] STEMP, University of the South Pacific, Suva, Fiji;School of Electrical and Electronic Engineering, Fiji National University, Suva, Fiji;

关键词: Human-computer interaction (HCI);    Brain wave;    Long short-term memory (LSTM);    Common spatial pattern (CSP);    Motor imagery (MI);    Informative frequency band (IFB);   

DOI  :  10.7717/peerj-cs.375

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown