High-Frequency Vibrating Stimuli Using the Low-Cost Coin-Type Motors for SSSEP-Based BCI

Steady-state somatosensory-evoked potential- (SSSEP-) based brain-computer interfaces (BCIs) have been applied for assisting people with physical disabilities since it does not require gaze fixation or long-time training. Despite the advancement of various noninvasive electroencephalogram- (EEG-) ba...

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Bibliographic Details
Published in:BioMed research international 2022-08, Vol.2022, p.1-10
Main Authors: Kim, Keun-Tae, Choi, Junhyuk, Jeong, Ji Hyeok, Kim, Hyungmin, Lee, Song Joo
Format: Article
Language:English
Subjects:
Algorithms
Attention
Classification
Cognition & reasoning
Disabilities
EEG
Electric motors
Electroencephalography
Experiments
Feasibility studies
Feature extraction
Filter banks
Fourier transforms
Frequency ranges
Human-computer interface
Interfaces
Low cost
Motors
Properties
Somatosensory evoked potentials
Stimuli
Support vector machines
Vibration
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Summary:Steady-state somatosensory-evoked potential- (SSSEP-) based brain-computer interfaces (BCIs) have been applied for assisting people with physical disabilities since it does not require gaze fixation or long-time training. Despite the advancement of various noninvasive electroencephalogram- (EEG-) based BCI paradigms, researches on SSSEP with the various frequency range and related classification algorithms are relatively unsettled. In this study, we investigated the feasibility of classifying the SSSEP within high-frequency vibration stimuli induced by a versatile coin-type eccentric rotating mass (ERM) motor. Seven healthy subjects performed selective attention (SA) tasks with vibration stimuli attached to the left and right index fingers. Three EEG feature extraction methods, followed by a support vector machine (SVM) classifier, have been tested: common spatial pattern (CSP), filter-bank CSP (FBCSP), and mutual information-based best individual feature (MIBIF) selection after the FBCSP. Consequently, the FBCSP showed the highest performance at 71.5±2.5% for classifying the left and right-hand SA tasks than the other two methods (i.e., CSP and FBCSP-MIBIF). Based on our findings and approach, the high-frequency vibration stimuli using low-cost coin motors with the FBCSP-based feature selection can be potentially applied to developing practical SSSEP-based BCI systems.
ISSN:2314-6133
2314-6141
DOI:10.1155/2022/4100381