Adaptive multi-degree of freedom Brain Computer Interface using online feedback: Towards novel methods and metrics of mutual adaptation between humans and machines for BCI
| Autor: | George K. Karavas, Chuong H. Nguyen, Panagiotis Artemiadis |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Man-Computer Interface
Male Physiology Computer science Social Sciences 02 engineering and technology computer.software_genre Topology Machine Learning 0302 clinical medicine Mutual adaptation Medicine and Health Sciences 0202 electrical engineering electronic engineering information engineering Manifolds Clinical Neurophysiology Brain Mapping Multidisciplinary Covariance matrix Brain Electroencephalography Adaptation Physiological Multi degree of freedom Electrophysiology Bioassays and Physiological Analysis Brain Electrophysiology Brain-Computer Interfaces Physical Sciences Medicine Engineering and Technology Female 020201 artificial intelligence & image processing Research Article Adult Computer and Information Sciences Imaging Techniques Science Geometry Neurophysiology Neuroimaging Research and Analysis Methods Machine learning 03 medical and health sciences Artificial Intelligence Support Vector Machines Tangents Speech Humans Learning Brain–computer interface Relevance Vector Machines business.industry Electrophysiological Techniques Biology and Life Sciences Confusion matrix Linguistics Support vector machine Speech Signal Processing Human Factors Engineering Signal Processing Artificial intelligence Clinical Medicine business computer Classifier (UML) Mathematics 030217 neurology & neurosurgery Neuroscience |
| Zdroj: | PLoS ONE PLoS ONE, Vol 14, Iss 3, p e0212620 (2019) |
| ISSN: | 1932-6203 |
| DOI: | 10.1371/journal.pone.0212620 |
| Popis: | This paper proposes a novel adaptive online-feedback methodology for Brain Computer Interfaces (BCI). The method uses ElectroEncephaloGraphic (EEG) signals and combines motor with speech imagery to allow for tasks that involve multiple degrees of freedom (DoF). The main approach utilizes the covariance matrix descriptor as feature, and the Relevance Vector Machines (RVM) classifier. The novel contributions include, (1) a new method to select representative data to update the RVM model, and (2) an online classifier which is an adaptively-weighted mixture of RVM models to account for the users' exploration and exploitation processes during the learning phase. Instead of evaluating the subjects' performance solely based on the conventional metric of accuracy, we analyze their skill's improvement based on 3 other criteria, namely the confusion matrix's quality, the separability of the data, and their instability. After collecting calibration data for 8 minutes in the first run, 8 participants were able to control the system while receiving visual feedback in the subsequent runs. We observed significant improvement in all subjects, including two of them who fell into the BCI illiteracy category. Our proposed BCI system complements the existing approaches in several aspects. First, the co-adaptation paradigm not only adapts the classifiers, but also allows the users to actively discover their own way to use the BCI through their exploration and exploitation processes. Furthermore, the auto-calibrating system can be used immediately with a minimal calibration time. Finally, this is the first work to combine motor and speech imagery in an online feedback experiment to provide multiple DoF for BCI control applications. |
| Databáze: | OpenAIRE |
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