Steady-State Somatosensory Evoked Potential-Based Brain-Computer Interface Using a Vibrotactile Stimulus by Mixing Vibration Frequencies

In the case of paralysis with visual and auditory impairments, brain-computer interface (BCI) based on tactile stimulus paradigms are alternative methods for controlling external devices. This study demonstrates using a BCI system via steady-state tactile stimulation for assistive technology and rehabilitation. We designed a steady-state tactile stimulator using a vibrotactile motor with two fundamental and mixing vibration frequency patterns. The vibrotactile stimulators were attached to the wrist. We recorded EEG signals and observed a response. We also verified the classification algorithm. The results revealed that the average classification accuracy ranged from 73.33-81.67%. We further implement in a real-time BCI for control and rehabilitation applications.

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Published
2024-11-21
DOI
https://doi.org/10.1109/bmeicon64021.2024.10896360
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
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article

Steady-State Somatosensory Evoked Potential-Based Brain-Computer Interface Using a Vibrotactile Stimulus by Mixing Vibration Frequencies

Manorot Borirakarawin, Nannaphat Siribunyaphat, Yunyong Punsawad
EEG and Brain-Computer Interfaces
article

Steady-State Somatosensory Evoked Potential-Based Brain-Computer Interface Using a Vibrotactile Stimulus by Mixing Vibration Frequencies

Manorot Borirakarawin, Nannaphat Siribunyaphat, Yunyong Punsawad
article en

Abstract

In the case of paralysis with visual and auditory impairments, brain-computer interface (BCI) based on tactile stimulus paradigms are alternative methods for controlling external devices. This study demonstrates using a BCI system via steady-state tactile stimulation for assistive technology and rehabilitation. We designed a steady-state tactile stimulator using a vibrotactile motor with two fundamental and mixing vibration frequency patterns. The vibrotactile stimulators were attached to the wrist. We recorded EEG signals and observed a response. We also verified the classification algorithm. The results revealed that the average classification accuracy ranged from 73.33-81.67%. We further implement in a real-time BCI for control and rehabilitation applications.

Nakhon Si Thammarat Rajabhat University (TH), Walailak University (TH)
Openalex Percentile: Top 72%
EEG and Brain-Computer Interfaces
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