TY - JOUR
T1 - A unified analytical framework with multiple fNIRS features for mental workload assessment in the prefrontal cortex
AU - Lim, Lam Ghai
AU - Ung, Wei Chun
AU - Chan, Yee Ling
AU - Lu, Cheng-Kai
AU - Sutoko, Stephanie
AU - Funane, Tsukasa
AU - Kiguchi, Masashi
AU - Tang, Tong Boon
N1 - Funding Information:
Manuscript received January 7, 2020; revised June 27, 2020 and August 15, 2020; accepted September 22, 2020. Date of publication September 28, 2020; date of current version November 6, 2020. This work was supported in part by the Yayasan UTP under Grant cost centre: 0153AA-E99, in part by the Ministry of Higher Education Malaysia under National Grant FRGS/1/2018/TK04/UTP/02/10, and in part by the Higher Institutional Centre of Excellence (HICoE) Grant awarded to the Centre for Intelligent Signal and Imaging Research (CISIR). (Corresponding author: Tong Boon Tang.) Lam Ghai Lim, Wei Chun Ung, Yee Ling Chan, and Tong Boon Tang are with the Centre for Intelligent Signal and Imaging Research (CISIR), Institute of Health and Analytics, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia (e-mail: limlamghai@ gmail.com; [email protected]; [email protected]; [email protected]).
Publisher Copyright:
© 2001-2011 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - Knowing the actual level of mental workload is important to ensure the efficacy of brain-computer interface (BCI) based cognitive training. Extracting signals from limited area of a brain region might not reveal the actual information. In this study, a functional near-infrared spectroscopy (fNIRS) device equipped with multi-channel and multi-distance measurement capability was employed for the development of an analytical framework to assess mental workload in the prefrontal cortex (PFC). In addition to the conventional features, e.g. hemodynamic slope, we introduced a new feature - deep contribution ratio which is the proportion of cerebral hemodynamics to the fNIRS signals. Multiple sets of features were examined by a simple logical operator to suppress the false detection rate in identifying the activated channels. Using the number of activated channels as input to a linear support vector machine (SVM), the performance of the proposed analytical framework was assessed in classifying three levels of mental workload. The best set of features involves the combination of hemodynamic slope and deep contribution ratio, where the identified number of activated channels returned an average accuracy of 80.6% in predicting mental workload, compared to a single conventional feature (accuracy: 59.8%). This suggests the feasibility of the proposed analytical framework with multiple features as a means towards a more accurate assessment of mental workload in fNIRS-based BCI applications.
AB - Knowing the actual level of mental workload is important to ensure the efficacy of brain-computer interface (BCI) based cognitive training. Extracting signals from limited area of a brain region might not reveal the actual information. In this study, a functional near-infrared spectroscopy (fNIRS) device equipped with multi-channel and multi-distance measurement capability was employed for the development of an analytical framework to assess mental workload in the prefrontal cortex (PFC). In addition to the conventional features, e.g. hemodynamic slope, we introduced a new feature - deep contribution ratio which is the proportion of cerebral hemodynamics to the fNIRS signals. Multiple sets of features were examined by a simple logical operator to suppress the false detection rate in identifying the activated channels. Using the number of activated channels as input to a linear support vector machine (SVM), the performance of the proposed analytical framework was assessed in classifying three levels of mental workload. The best set of features involves the combination of hemodynamic slope and deep contribution ratio, where the identified number of activated channels returned an average accuracy of 80.6% in predicting mental workload, compared to a single conventional feature (accuracy: 59.8%). This suggests the feasibility of the proposed analytical framework with multiple features as a means towards a more accurate assessment of mental workload in fNIRS-based BCI applications.
KW - Brain-computer interface
KW - deep contribution ratio
KW - functional near-infrared spectroscopy
KW - mental workload
KW - prefrontal cortex
KW - working memory
UR - http://www.scopus.com/inward/record.url?scp=85095862002&partnerID=8YFLogxK
U2 - 10.1109/TNSRE.2020.3026991
DO - 10.1109/TNSRE.2020.3026991
M3 - Article
C2 - 32986555
AN - SCOPUS:85095862002
SN - 1534-4320
VL - 28
SP - 2367
EP - 2376
JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering
JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering
IS - 11
ER -