Abstract
Near-infrared spectroscopy (NIRS) has been recently investigated for use in noninvasive brain-computer interface (BCI) technologies. Previous studies have demonstrated the ability to classify patterns of neural activation associated with different mental tasks (e.g., mental arithmetic) using NIRS signals. Though these studies represent an important step towards the realization of an NIRS-BCI, there is a paucity of literature regarding the consistency of these responses, and the ability to classify them on a single-trial basis, over multiple sessions. This is important when moving out of an experimental context toward a practical system, where performance must be maintained over longer periods. When considering response consistency across sessions, two questions arise: 1) can the hemodynamic response to the activation task be distinguished from a baseline (or other task) condition, consistently across sessions, and if so, 2) are the spatiotemporal characteristics of the response which best distinguish it from the baseline (or other task) condition consistent across sessions. The answers will have implications for the viability of an NIRS-BCI system, and the design strategies (especially in terms of classifier training protocols) adopted. In this study, we investigated the consistency of classification of a mental arithmetic task and a no-control condition over five experimental sessions. Mixed model linear regression on intrasession classification accuracies indicate that the task and baseline states remain differentiable across multiple sessions, with no significant decrease in accuracy (p = 0.67). Intersession analysis, however, revealed inconsistencies in spatiotemporal response characteristics. Based on these results, we investigated several different practical classifier training protocols, including scenarios in which the training and test data come from 1) different sessions, 2) the same session, and 3) a combination of both. Results indicate that when selecting optimal classifier training protocols for NIRS-BCI, a compromise between accuracy and convenience (e.g., in terms of duration/frequency of training data collection) must be considered.
Highlights
Many individuals with severe and multiple motor disabilities rely entirely on access devices for communication and environmental control
To answer our second question regarding the consistency of the discriminatory response characteristics across multiple sessions, we examined whether or not classification accuracy changed significantly when training and test data came from different sessions as compared to when they came from the same session
Consistency of Response Detection Across Sessions This study represents the first investigation into the consistency of single-trial classification of task-induced prefrontal activity using near infrared spectroscopy (NIRS) over multiple sessions
Summary
Many individuals with severe and multiple motor disabilities rely entirely on access devices (e.g., mechanical switches, eyetrackers) for communication and environmental control. For individuals who have retained no reliable, voluntary motor control in any part of the body (e.g., as in total locked-in syndrome), conventional access devices, which are primarily movement-based, are ineffective. Brain-computer interface (BCI) technologies are controlled via brain activity alone, and may provide these individuals with an alternative, movement-free means of access. A user controls a BCI output by consciously eliciting distinct, reproducible patterns of activation in a particular region of the brain, which is usually achieved by performing different mental tasks (e.g., motor imagery). The system detects and interprets these task-induced patterns of activation, and produces the appropriate command signal to control a connected external device (e.g., computer cursor) in the way the user intended.
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