A Study of Spatial Feature Conservation in Reduced Channels of the Electroencephalogram–Functional Near-Infrared Spectroscopy-Based Brain–Computer Interface Using Deep Learning

The state-of-art Hybrid Brain–Computer Interface (BCI) has shown improved classification of mental states either by combining different modalities or by choosing a combination of BCI activation tasks. Among these, the classification of motor imagery/execution tasks of contralateral and ipsilateral data of the upper arm is challenging due to its spatial adjacency and retention of these spatial features. The proposed work uses a Hybrid BCI dataset acquired using EEG and fNIRS for upper limb movement (right hand/left hand, right arm/left arm). The electrode positioning is along the motor cortex, and previous deep learning studies have shown that good accuracy can be obtained without any channel selection. Hence, the current study applies a combination of deep learning methods to the data, which were halved into two without using channel selection algorithms. The model was evaluated for both sets of channels using F1-score, precision, and recall with an accuracy of 89%. This investigation shows that all the channels of the studied dataset contained inter-related spatial information. Also, the problem of long-term EEG/fNIRS recording can be addressed using this study if the total number of channels can be used in two halves by switching the channels after the minimum efficient time of recording.