Abstract
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.
Highlights
A human–computer interaction (HCI) system uses cutting-edge techniques for establishing direct communication between the human brain and a computer (Li et al, 2016)
The proposed method has been able to successfully optimize the filter band parameters that accounts for its improved performance in comparison to the other state-of-the-art methods
OPTICAL+ achieved the lowest error rate of 30.41%, which is an improvement of 1.40% compared to the OPTICAL predictor and an improvement of 3.66% compared to the conventional wide-band Common spatial pattern (CSP) approach
Summary
A human–computer interaction (HCI) system uses cutting-edge techniques for establishing direct communication between the human brain and a computer (Li et al, 2016). The drawback of using non-invasive sensors is that it is
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