Year: 2024
Type: article
Abstract: Ear electroencephalogram (ear-EEG) records electrical signals around the ear, offering a more casual and user-friendly approach to EEG measurement. Steady-state visual evoked potential (SSVEP) are brain responses elicited by gazing at flickering stimuli. Ear-EEG can enhance comfort in SSVEP-based brain–computer interface (SSVEP-BCI), but its performance is typically low behind traditional SSVEP-BCI. Additionally, predicting the performance of ear-EEG SSVEP-BCIs before experimentation is challenging, often increasing design costs. This study proposes the SSVEP ratio as a supplementary index to traditional metrics such as information transfer rate (ITR) and BCI accuracy. Using the SSVEP ratio and the KNN algorithm, we predicted BCI accuracy and ITR, aiming to lower design costs. The developed four-inputs ear-EEG SSVEP-BCI achieved a maximum BCI accuracy of 89.17 ± 3.62% and an ITR of 10.60 ± 0.36 bits/min. Predicted BCI accuracy was 90.21 ± 3.25% and an ITR was 9.43 ± 0.96 bits/min in ear-EEG SSVEP-BCI. Predicted values matched the actual results, demonstrating that the SSVEP ratio can effectively predict BCI accuracy, thereby streamlining the design process for ear-EEG SSVEP-BCI.
Source: Artificial Life and Robotics
Authors: Sodai Kondo, Hideyuki Harafuji, Hisaya Tanaka
Institutions: Kogakuin University, Kokugakuin University
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $3,090
OpenAlex ID: W4405777414