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Continuous EEG Classification for a Self-paced BCI

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Satti, A.R., Coyle, D and Prasad, G (2009) Continuous EEG Classification for a Self-paced BCI. In: 2009. NER '09. 4th International IEEE/EMBS Conference on Neural Engineering,. IEEE. 4 pp. [Conference contribution]

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URL: http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=5109296&searchWithin%3DAuthors%3A.QT.+Prasad%2C+G..QT.%26searchWithin%3DContinuous+EEG+Classification+for+a+Self-paced+BCI%26openedRefinements%3D*%26searchField%3DSearch+All

DOI: 10.1109/NER.2009.5109296

Abstract

Transferring electroencephalogram (EEG)-based brain-computer interface (BCI) systems from synchronous laboratory conditions to real-world applications and situations demands the continuous detection of brain patterns in which the user is in control of the timing and pace of the BCI instead of the computer. A self-paced BCI requires continuous analysis of the continuing brain activity, however, not only the intentional-control (IC) states have to be detected (e.g., motor imagery and imagination) but also the inactive periods, where the user is in a non-control state (NC). The nonstationary nature of the brain signals provides a rather unstable input resulting in uncertainty and complexity in the control. Intelligent processing algorithms adapted to the task at hand are a prerequisite for reliable self-paced BCI applications. This work presents a novel intelligent processing strategy for the realization of an effective self-paced BCI which has the capability to reduce noise as well as adaptation to continuous online biasing. A Savitzki-Golay filter has been applied to remove spikes/outliers while preserving the feature set structure. An anti-bias system is introduced which readjusts the classification output based on the brain's current and previous states. Furthermore, a multiple threshold algorithm is applied on the resultant unbiased classifier output for improved accuracy. These algorithms are tested on 4 real and 3 artificial datasets and results shown are considerably promising and demonstrate the significance of the proposed intelligent and adaptive algorithms.

Item Type:Conference contribution (Poster)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:12217
Deposited By:Professor Girijesh Prasad
Deposited On:09 Mar 2010 12:06
Last Modified:15 Mar 2011 16:05

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