TY - JOUR
T1 - Classification of Movement Preparation between Attended and Distracted Self-Paced Motor Tasks
AU - Aliakbaryhosseinabadi, S. Susan
AU - Kamavuako, E. N.
AU - Jiang, N.
AU - Farina, D.
AU - Mrachacz-Kersting, N.
PY - 2019/11
Y1 - 2019/11
N2 - OBJECTIVE: Brain-computer interface (BCI) systems aim to control external devices by using brain signals. The performance of these systems is influenced by the user's mental state, such as attention. In this study, we classified two attention states to a target task (attended and distracted task level) while attention to the task is altered by one of three types of distractors. METHODS: A total of 27 participants were allocated into three experimental groups and exposed to one type of distractor. An attended condition that was the same across the three groups comprised only the main task execution (self-paced dorsiflexion) while the distracted condition was concurrent execution of the main task and an oddball task (dual-task condition). Electroencephalography signals were recorded from 28 electrodes to classify the two attention states of attended or distracted task conditions by extracting temporal and spectral features. RESULTS: The results showed that the ensemble classification accuracy using the combination of temporal and spectral features (spectro-temporal features, 82.3 ± 2.7%) was greater than using temporal (69 ± 2.2%) and spectral (80.3 ± 2.6%) features separately. The classification accuracy was computed using a combination of different channel locations, and it was demonstrated that a combination of parietal and centrally located channels was superior for classification of two attention states during movement preparation (parietal channels: 84.6 ± 1.3%, central and parietal channels: 87.2 ± 1.5%). CONCLUSION: It is possible to monitor the users' attention to the task for different types of distractors. SIGNIFICANCE: It has implications for online BCI systems where the requirement is for high accuracy of intention detection.
AB - OBJECTIVE: Brain-computer interface (BCI) systems aim to control external devices by using brain signals. The performance of these systems is influenced by the user's mental state, such as attention. In this study, we classified two attention states to a target task (attended and distracted task level) while attention to the task is altered by one of three types of distractors. METHODS: A total of 27 participants were allocated into three experimental groups and exposed to one type of distractor. An attended condition that was the same across the three groups comprised only the main task execution (self-paced dorsiflexion) while the distracted condition was concurrent execution of the main task and an oddball task (dual-task condition). Electroencephalography signals were recorded from 28 electrodes to classify the two attention states of attended or distracted task conditions by extracting temporal and spectral features. RESULTS: The results showed that the ensemble classification accuracy using the combination of temporal and spectral features (spectro-temporal features, 82.3 ± 2.7%) was greater than using temporal (69 ± 2.2%) and spectral (80.3 ± 2.6%) features separately. The classification accuracy was computed using a combination of different channel locations, and it was demonstrated that a combination of parietal and centrally located channels was superior for classification of two attention states during movement preparation (parietal channels: 84.6 ± 1.3%, central and parietal channels: 87.2 ± 1.5%). CONCLUSION: It is possible to monitor the users' attention to the task for different types of distractors. SIGNIFICANCE: It has implications for online BCI systems where the requirement is for high accuracy of intention detection.
KW - Task analysis
KW - Visualization
KW - Electroencephalography
KW - Electromyography
KW - Electrodes
KW - Neurons
KW - Synchronous motors
KW - Attention diversion
KW - Classification of movement preparation
KW - Brain-computer interface (BCI)
KW - Channel selection
KW - Dual tasking
KW - Attention diversion
KW - brain-computer interface (BCI)
KW - channel selection
KW - classification of movement preparation
KW - dual tasking
UR - http://www.scopus.com/inward/record.url?scp=85073656001&partnerID=8YFLogxK
U2 - 10.1109/TBME.2019.2900206
DO - 10.1109/TBME.2019.2900206
M3 - Journal article
SN - 0018-9294
VL - 66
SP - 3060
EP - 3071
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 11
M1 - 8643788
ER -