Optimized EEG–fNIRS Based Mental Workload Detection Method for Practical Applications

Abstract

Background: Mental workload is a critical consideration in complex man–machine systems design. Among various mental workload detection techniques, multimodal detection techniques integrating EEG and fNIRS signals have attracted considerable attention. However, existing EEG–fNIRS-based mental workload detection methods have certain defects, such as complex signal acquisition channels and low detection accuracy, which restrict their practical application.Method: The signal acquisition configuration was optimized and a more accurate and convenient EEG–fNIRS-based mental workload detection method was constructed. A classical MATB task was conducted with 20 participating volunteers. Subjective scale data, 64-channel EEG data, and two-channel fNIRS data were collected.Results: A higher number of EEG channels correspond to higher detection accuracy. However, there is no obvious improvement in accuracy once the number of EEG channels reaches 26, with a four-level mental workload detection accuracy of 78.25±4.71%. Partial results of physiological analysis verify the results of previous studies, such as that the θ power of EEG and concentration of O2Hb in the prefrontal region increase while the concentration of HHb decreases with task difficulty. It was further observed, for the first time, that the energy of each band of EEG signals was significantly different in the occipital lobe region, and the power of 𝛽1 and 𝛽2 bands in the occipital region increased significantly with task difficulty. The changing range and the mean amplitude of O2Hb in high-difficulty tasks were significantly higher compared with those in low-difficulty tasks.Conclusions: The channel configuration of EEG–fNIRS-based mental workload detection was optimized to 26 EEG channels and two frontal fNIRS channels. A four-level mental workload detection accuracy of 78.25±4.71% was obtained, which is higher than previously reported results. The proposed configuration can promote the application of mental workload detection technology in military, driving, and other complex human–computer interaction systems.

EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning

“…It meant that different methods explored distinct information and became complementary to each other thereby improving classification performance. The results agreed with the conclusion in the literature [ 29 , 38 , 39 , 42 ]. The present paper is an advance on previous studies because it generated new knowledge about regional information by comparing the foci of independent types of features.…”

“…Brouwer et al [ 33 ] found the alpha power of the midline parietal (Pz) region in EEG recordings significantly decreased with memory load, effectively distinguishing 2-back from 0-back. Chu et al [ 39 ] stated that the alpha-power of O1 indicated differences between multi-level workloads. Regarding fNIRS, the prefrontal areas were well-accepted for measuring variations in mental workload [ 40 , 41 , 42 ].…”