뇌-컴퓨터 인터랙션 오탐 저감을 위한 뇌파-턱 근전도 통합 신호 제어 프로토콜 개발 (Development of SSVEP Command Protocol Combined with EMG Characterized by Jaw-Clenching to Reduce False Alarms in Brain-Computer Interaction)

서지정보

· 발행기관 : 대한인간공학회
· 수록지 정보 : 대한인간공학회지 / 41권 / 3호 / 237 ~ 249페이지
· 저자명 : 채민성, 박상인, 문성철

초록

(EMG) induced by jaw-clenching can be accurately observed in steady-state visually evoked potential (SSVEP) signals at occipital lobes as well as temporal lobes. We also investigated a feasibility that the fused SSVEP data with the jaw-clenching EMG can be classified as a function of task types characterized by SSVEPs with and without the short and long clenching noise. Based on this hypothesis, we proposed a novel command protocol to effectively and efficiently reduce false alarms caused by using SSVEP brain-computer interface (BCI).
Background: Much attention has been paid to various brain-computer interfaces for neural rehabilitation and alternative communication channels for disabled people with severe neurological disorders. However, despite the interests in BCI, false alarms caused by interference effects between command flickers with different frequencies or internal and external noises have been still problematic and have yet to be investigated. Even though protocols to correct the false alarms based on error-related potential (ErrP) or eye blinks have been proposed, it has still limitation due to errors inherent in involuntary reactions of humans. Thus, it is necessary to develop a protocol to address the issue and propose a method insusceptible to the involuntary reactions.
Method: Nine undergraduate students (3 female) voluntarily participated in the experiment. They were asked to divide their attention between the two squares randomly oscillating with 8.57 and 10Hz and perform three types of tasks characterized by SSVEP only, short clenching, and long clenching conditions. They carried out the three tasks with a semi-counterbalanced and random order and rest interval between the tasks was set to 10 minutes to minimize order and carryover effects. It took about 12 minutes in completing the experimental tasks for each participant. We used five machine learning algorithms of binary classification to compare the performance in classifying the brainwave signals characterized by the three tasks.
Results: As a result of the classification comparison, random forest showed the highest classification performance of more than 0.85 (AUC) as follows: 0.88 (short clenching vs. SSVEP), 0.87 (long clenching vs. SSVEP), 0.85 (long clenching vs. short clenching condition) in the test data set. Notably, the findings implicated a clear classification in the long cand short clenching condition even at the frequency band of more than 20Hz. For the participants whose highest weight factor was shown at the occipital lobe, it can be possible to reduce false alarms in the SSVEP BCI with only single electrode at the occipital lobe.
Conclusion: The findings found in this study implicate that the proposed protocol can be used to effectively and efficiently correct false alarms in event-related potential (ERP), event-related desynchronization/synchronization (ERD/ERS) BCI as well as SSVEP BCI. On top of that, classifying the subtle changes in the jaw clenching types enables BCI developers to create additional commands to modulate their BCI system. The results can be greatly improved if weight factors in the binary classification are modulated as a function of individual characteristics.
Application: The findings obtained in this study can be utilized in developing asynchronous BCI very robust to false alarms and expected to provide a future direction into hybrid BCI system.

영어초록

(EMG) induced by jaw-clenching can be accurately observed in steady-state visually evoked potential (SSVEP) signals at occipital lobes as well as temporal lobes. We also investigated a feasibility that the fused SSVEP data with the jaw-clenching EMG can be classified as a function of task types characterized by SSVEPs with and without the short and long clenching noise. Based on this hypothesis, we proposed a novel command protocol to effectively and efficiently reduce false alarms caused by using SSVEP brain-computer interface (BCI).
Background: Much attention has been paid to various brain-computer interfaces for neural rehabilitation and alternative communication channels for disabled people with severe neurological disorders. However, despite the interests in BCI, false alarms caused by interference effects between command flickers with different frequencies or internal and external noises have been still problematic and have yet to be investigated. Even though protocols to correct the false alarms based on error-related potential (ErrP) or eye blinks have been proposed, it has still limitation due to errors inherent in involuntary reactions of humans. Thus, it is necessary to develop a protocol to address the issue and propose a method insusceptible to the involuntary reactions.
Method: Nine undergraduate students (3 female) voluntarily participated in the experiment. They were asked to divide their attention between the two squares randomly oscillating with 8.57 and 10Hz and perform three types of tasks characterized by SSVEP only, short clenching, and long clenching conditions. They carried out the three tasks with a semi-counterbalanced and random order and rest interval between the tasks was set to 10 minutes to minimize order and carryover effects. It took about 12 minutes in completing the experimental tasks for each participant. We used five machine learning algorithms of binary classification to compare the performance in classifying the brainwave signals characterized by the three tasks.
Results: As a result of the classification comparison, random forest showed the highest classification performance of more than 0.85 (AUC) as follows: 0.88 (short clenching vs. SSVEP), 0.87 (long clenching vs. SSVEP), 0.85 (long clenching vs. short clenching condition) in the test data set. Notably, the findings implicated a clear classification in the long cand short clenching condition even at the frequency band of more than 20Hz. For the participants whose highest weight factor was shown at the occipital lobe, it can be possible to reduce false alarms in the SSVEP BCI with only single electrode at the occipital lobe.
Conclusion: The findings found in this study implicate that the proposed protocol can be used to effectively and efficiently correct false alarms in event-related potential (ERP), event-related desynchronization/synchronization (ERD/ERS) BCI as well as SSVEP BCI. On top of that, classifying the subtle changes in the jaw clenching types enables BCI developers to create additional commands to modulate their BCI system. The results can be greatly improved if weight factors in the binary classification are modulated as a function of individual characteristics.
Application: The findings obtained in this study can be utilized in developing asynchronous BCI very robust to false alarms and expected to provide a future direction into hybrid BCI system.

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