Signal Processing for Hybrid BCI Signals

The brain signals can be converted to a command to control some external device using a brain-computer interface system. The unimodal BCI system has limitations like the compensation of the accuracy with the increase in the number of classes. In addition to this many of the acquisition systems are n...

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Bibliographic Details
Published in:Journal of physics. Conference series 2022-08, Vol.2318 (1), p.12007
Main Authors: Shelishiyah, R, Bharani Dharan, M, Kishore Kumar, T, Musaraf, R, Beeta, Thiyam Deepa
Format: Article
Language:English
Subjects:
Bandpass filters
Bouguer law
Electroencephalography
Feature extraction
Hemoglobin
Human-computer interface
Infrared spectroscopy
Near infrared radiation
Physics
Signal processing
Signal to noise ratio
Temporal resolution
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Summary:The brain signals can be converted to a command to control some external device using a brain-computer interface system. The unimodal BCI system has limitations like the compensation of the accuracy with the increase in the number of classes. In addition to this many of the acquisition systems are not robust for real-time application because of poor spatial or temporal resolution. To overcome this, a hybrid BCI technology that combines two acquisition systems has been introduced. In this work, we have discussed a preprocessing pipeline for enhancing brain signals acquired from fNIRS (functional Near Infrared Spectroscopy) and EEG (Electroencephalography). The data consists of brain signals for four tasks – Right/Left hand gripping and Right/Left arm raising. The EEG (brain activity) data were filtered using a bandpass filter to obtain the activity of mu (7-13 Hz) and beta (13-30 Hz) rhythm. The Oxy-haemoglobin and Deoxy-haemoglobin (HbO and HbR) concentration of the fNIRS signal was obtained with Modified Beer Lambert Law (MBLL). Both signals were filtered using a fifth-order Butterworth band pass filter and the performance of the filter is compared theoretically with the estimated signal-to-noise ratio. These results can be used further to improve feature extraction and classification accuracy of the signal.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2318/1/012007