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A Comparative Performance Study on the Time Intervals of Hybrid Brain–Computer Interface Signals

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Abstract

Acquiring motor events with EEG and fNIRS have proven to be the most convenient and cost effective method. EEG explains an event with respect to a depolarization occurring when a task was done giving information on the instance of brain’s response to a certain stimuli. On the other hand, fNIRS interprets the same as rise in metabolic activity in a certain part of the cerebral cortex, explaining the location which is activated by the stimuli. The results from these single modalities compliment resulting in lower classification accuracy. The combined modalities for brain signal acquisition provide a better classification accuracy as compared to single modality, with a reduced number of features since both spatial and temporal information is achieved. In this study, we used simultaneous data from EEG and fNIRS modalities. This combination when used for motor tasks, especially hand movements and arm movement shows an improved classification since the arm and hand spatially lie adjacent to each other and it is challenging to use EEG alone to resolve the right/left arm/hand movement. The current work shows that choice of time intervals of task performance can give better results with second and higher order features taken using Thin ICA instead of taking the entire task data. This would not only reduce computational load, but will also improve the command generation time in future.

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Data availability

The dataset generated and analyzed during the current study are available from the corresponding author on reasonable request.

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  1. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai – 62, India

    R. Shelishiyah & Thiyam Deepa Beeta

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  1. R. Shelishiyah
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  2. Thiyam Deepa Beeta
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Correspondence to R. Shelishiyah.

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This article is part of the topical collection “Advances in Computational Approaches for Image Processing, Wireless Networks, Cloud Applications and Network Security” guest edited by P. Raviraj, Maode Ma and Roopashree H R.

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Shelishiyah, R., Beeta, T.D. A Comparative Performance Study on the Time Intervals of Hybrid Brain–Computer Interface Signals. SN COMPUT. SCI. 4, 771 (2023). https://doi.org/10.1007/s42979-023-02255-5

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