Simplification of EEG Signal Extraction, Processing, and Classification Using a Consumer-Grade Headset to Facilitate Student Engagement in BCI Research

摘要

Brain-computer interfaces (BCIs) are an emerging technology that leverage neurophysiological signals as input to computing systems. By circumventing the reliance on traditional input methods (e.g., mouse and keyboard), BCIs show a promising alternative interaction modality for people with disabilities. Advances in BCI research have further inspired a range of novel applications, such as the use of neurophysiological signals as passive input (e.g., to detect and reduce operator workload when managing multiple machines). BCIs have also emerged as a tool for student engagement due to the intrinsic interdisciplinarity of the technology, which spans the fields of computer science, electrical engineering, neuroscience, psychology and their broad applicability. However, these benefits also stand as a challenge to students interested in BCI research, as the need for familiarity with multiple related disciplines creates a high barrier to entry. Towards overcoming this barrier, we developed a simplified EEG-based BCI wherein we integrated a low-cost, consumer-grade headset for signal extraction with a novel graphical user interface that affords seamless exploration of several signal processing and machine learning techniques for analysis. Here, electrical activity is measured in real-time via an extracortical electrode placed on the user's forehead, superior to the prefrontal cortex. The headset can then be connected to any Bluetooth-compatible device via a Bluetooth connection for (1) processing and classification of the signal contents and (2) operation of a machine (e.g., the Cozmo robot) via the intentional brain activity of the user. An additional visualization model also allows the user to explore the signal processing techniques, including the information decomposition and classification.