Cost-Efficient, Portable, and Custom Multi-Subject Electroencephalogram Recording System

• Published in: IEEE access Vol. 7; pp. 56760 - 56769
• Main Authors: Chuang, Kai-Chiang, Lin, Yuan-Pin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Amplifiers; Brain-computer interface; Electrodes; electroencephalogram; Electroencephalography; Electronic components; event-related potential; Hardware; Headphones; Human-computer interface; multi-subject recording; Radio frequency; Signal quality; Stability analysis; Synchronism; Synchronization; Three-dimensional displays
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2914088

Multi-subject electroencephalogram (EEG) computing has attracted increasing interest in recent years. Commercial EEG headsets often lead to a space-required or cost-expensive setup for obtaining simultaneous recordings of multiple subjects. The sparsity or immobility of their electrode placements may compromise their realistic applicability. Thus, this work developed a cost-efficient, portable, and customizable system to mitigate these constraints. The developed system implemented two core hardware infrastructures, including an event broadcaster and a dry electrode-compatible EEG amplifier by assembling entirely low-cost, off-the-shelf electronic components. The broadcaster allowed distribution of event markers to multiple EEG amplifiers concurrently, whereas the amplifier transmitted the digitized, event-synchronized EEG signals wirelessly. By conducting an oddball event-related potential (ERP) experiment with simultaneous recordings of three subjects on 10 days, the system reliably captured the time- and phase-locked ERP components (e.g., N100 and P300 amplitudes) by single-subject, multi-subject, and multi-day analytical approaches. The practicality and stability of the proposed system was empirically demonstrated in terms of the signal quality, EEG-event synchronization, and inter-amplifier coordination for a multi-subject setup. This work sheds light on how to economically facilitate and scale up multi-subject EEG computing for fundamental research and for brain-computer interface (BCI) applications in a larger subject population.