A subject-transfer framework for obviating inter- and intra-subject variability in EEG-based drowsiness detection

• Published in: NeuroImage (Orlando, Fla.) Vol. 174; pp. 407 - 419
• Main Authors: Wei, Chun-Shu, Lin, Yuan-Pin, Wang, Yu-Te, Lin, Chin-Teng, Jung, Tzyy-Ping
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.07.2018; Elsevier Limited
• Subjects: Brain-computer interface (BCI); Calibration; Data processing; Drowsiness; EEG; EEG baseline; Electroencephalogram (EEG); Electroencephalography; Hierarchical cluster analysis (HCA); Interfaces; Subject-transfer decoding; Drowsiness; Brain-computer interface (BCI); Subject-transfer decoding; Electroencephalogram (EEG); Hierarchical cluster analysis (HCA); EEG baseline
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2018.03.032

Inter- and intra-subject variability pose a major challenge to decoding human brain activity in brain-computer interfaces (BCIs) based on non-invasive electroencephalogram (EEG). Conventionally, a time-consuming and laborious training procedure is performed on each new user to collect sufficient individualized data, hindering the applications of BCIs on monitoring brain states (e.g. drowsiness) in real-world settings. This study proposes applying hierarchical clustering to assess the inter- and intra-subject variability within a large-scale dataset of EEG collected in a simulated driving task, and validates the feasibility of transferring EEG-based drowsiness-detection models across subjects. A subject-transfer framework is thus developed for detecting drowsiness based on a large-scale model pool from other subjects and a small amount of alert baseline calibration data from a new user. The model pool ensures the availability of positive model transferring, whereas the alert baseline data serve as a selector of decoding models in the pool. Compared with the conventional within-subject approach, the proposed framework remarkably reduced the required calibration time for a new user by 90% (18.00 min–1.72 ± 0.36 min) without compromising performance (p = 0.0910) when sufficient existing data are available. These findings suggest a practical pathway toward plug-and-play drowsiness detection and can ignite numerous real-world BCI applications. •A novel subject-transfer framework for reducing calibration time in brain state decoding.•Feasibility of cross-subject model transferring inferred from hierarchical clustering.•Robust decoding performance supported by large-scale existing data.•Significant decrease in calibration time using baseline brain activity.