Effect of ocular artifact removal in brain computer interface accuracy

Thulasidas, M.; Guan, Cuntai; Ranganatha, S; Wu, Jiankang; Zhu, Xiangyang; Wang, Xu

doi:10.1109/iembs.2004.1404220

Cited by 11 publications

(8 citation statements)

References 7 publications

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“…Upcoming consumer application based on EEG such as brain-computer interface [1] or biofeedback devices [2] are mostly intended for non-clinical, everyday home use. Simplicity of use and low price are essential for those devices.…”

Section: Problem Descriptionmentioning

confidence: 99%

An Algebraic Method for Eye Blink Artifacts Detection in Single Channel EEG Recordings

et al. 2010

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Section: Problem Descriptionmentioning

confidence: 99%

An Algebraic Method for Eye Blink Artifacts Detection in Single Channel EEG Recordings

et al. 2010

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“…However, more sophisticated methods such as principal component analysis (PCA) and independent component analysis (ICA) are popular to reduce ECG and EMG noises that have overlapping spectral information with EEG. Another common artifact that corrupts EEG signals is eye blinks; many techniques have been proposed to solve this problem (Thulasidas et al, 2004).…”

Section: Pre-processingmentioning

confidence: 99%

Current Practices in Electroencephalogram- Based Brain-Computer Interfaces

Palaniappan

Syan

Raveendran

2009

Encyclopedia of Information Science and Technology, Second Edition

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Electroencephalogram (EEG) is the electrical activity of the brain recorded by electrodes placed on the scalp. EEG signals are generally investigated for the diagnosis of mental conditions such as epilepsy, memory impairments, and sleep disorders. In recent years there has been another application using EEG: for brain-computer interface (BCI) designs (Vaughan & Wolpaw, 2006). EEG-based BCI designs are very useful for hands-off device control and communication as they use the electrical activity of the brain to interface with the external environment, therefore circumventing the use of peripheral muscles and limbs. Some current applications of BCIs in communication systems are for paralyzed individuals to communicate with their surroundings through character/menu selection and in device control such as wheelchair movement, prosthetics control, and flight and rehabilitative (assistive) technologies. For the general public, some of the possible applications are hands-off menu selection, flight/space control, and virtual reality (entertainment). BCI has also been applied in biometrics (Palaniappan & Mandic, 2007).

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“…However, EEG measures are always contaminated by non-cerebral signals, which may disturb the interpretation of the brain activity. This issue has become a recurrent problem, for example in Brain-Computer Interfaces (BCI), where it has been proved to decrease classification error rates [11].…”

Section: Introductionmentioning

confidence: 99%

Independent Component Analysis and Signal Separation

Jutten

Romano

Barros

2009

Lecture Notes in Computer Science

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The estimation of relevant information theoretical quantities , such as entropy, mutual information, and various divergences is computationally expensive in high dimensions. However, for this task, one may apply pairwise Euclidean distances of sample points, which suits random projection (RP) based low dimensional embeddings. The Johnson-Lindenstrauss (JL) lemma gives theoretical bound on the dimension of the low dimensional embedding. We adapt the RP technique for the estimation of information theoretical quantities. Intriguingly, we find that embeddings into extremely small dimensions, far below the bounds of the JL lemma, provide satisfactory estimates for the original task. We illustrate this in the Independent Subspace Analysis (ISA) task; we combine RP dimension reduction with a simple ensemble method. We gain considerable speed-up with the potential of real-time parallel estimation of high dimensional information theoretical quantities.

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Effect of ocular artifact removal in brain computer interface accuracy

Cited by 11 publications

References 7 publications

An Algebraic Method for Eye Blink Artifacts Detection in Single Channel EEG Recordings

An Algebraic Method for Eye Blink Artifacts Detection in Single Channel EEG Recordings

Current Practices in Electroencephalogram- Based Brain-Computer Interfaces

Independent Component Analysis and Signal Separation

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