OPTICAL+: a frequency-based deep learning scheme for recognizing brain wave signals.
Shiu KumarRonesh SharmaAlok SharmaPublished in: PeerJ. Computer science (2021)
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.
Keyphrases
- deep learning
- machine learning
- resting state
- white matter
- convolutional neural network
- artificial intelligence
- high resolution
- functional connectivity
- cerebral ischemia
- physical activity
- high speed
- endothelial cells
- healthcare
- sleep quality
- nuclear factor
- gene expression
- immune response
- neural network
- magnetic resonance imaging
- working memory
- mass spectrometry
- depressive symptoms
- dna methylation
- loop mediated isothermal amplification
- real time pcr
- label free
- temporal lobe epilepsy
- inflammatory response
- copy number