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Investigation of EEG Correlate in NIRS Signal for BCI

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Advances in Neuroergonomics and Cognitive Engineering (AHFE 2020)

Abstract

In this study, a unique approach has been presented for performing correlation between the signals of two neuroimaging modalities for motor imagery data. By correlating the signals, we investigate the time sequence relationship between the haemodynamic response and electrophysiological activity during the performance of mental arithmetic vs resting-state and motor imagery activity of right vs left hand, as we correlate near-infrared spectroscopy (NIRS) and electroencephalography (EEG) signals respectively obtained during the same activity. Data of both EEG and NIRS are taken from “Open Access Dataset for EEG+NIRS Single-Trial Classification”. Thirty active electrodes have been used for the EEG data extraction using 1000 Hz as the sampling frequency. International 10-5 system has been employed for electrode placement for EEG signal extraction and one-tailed Pearson’s correlation analysis has been exercised on the responses of prominent channels. By correlating the EEG-NIRS signals, we demonstrate the correlation between haemodynamic response and readiness potential (RP) in the premotor cortex. Both modalities are also used for Mental Workload assessment so our work proves helpful in MWL results extraction too. The suggested correlation method can be utilized for approach validation procedures in future multi-modal BCI research activities.

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Acknowledgments

We would like to acknowledge School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Pakistan and European Union (EU)’s Horizon 2020, Research and Innovation Staff Exchange Evaluations (RISE) under grant agreement No 823904 - ENHANCE project (MSCA-RISE 823904) for technical support and funding.

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Correspondence to Ahmed Husnain Johar .

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Johar, A.H. et al. (2021). Investigation of EEG Correlate in NIRS Signal for BCI. In: Ayaz, H., Asgher, U. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1201. Springer, Cham. https://doi.org/10.1007/978-3-030-51041-1_42

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