Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG.
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TLDR
Effective combinations of computational methods provide possible classification of human movement intention from single trial EEG with reasonable accuracy and could be the basis for a potential brain-computer interface based on human natural movement, which might reduce the requirement of long-term training.About:
This article is published in Clinical Neurophysiology.The article was published on 2007-12-01 and is currently open access. It has received 129 citations till now. The article focuses on the topics: Body movement & Mahalanobis distance.read more
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A comprehensive review of EEG-based brain-computer interface paradigms.
TL;DR: The current review evaluates EEG-based BCI paradigms regarding their advantages and disadvantages from a variety of perspectives, and various EEG decoding algorithms and classification methods are evaluated.
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Detection of self-paced reaching movement intention from EEG signals
TL;DR: The results suggest that the potential use of brain signals recorded non-invasively for detecting the time before a self-paced reaching movement is initiated could have a direct impact on advancing robot-assisted neurorehabilitation.
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Electroencephalography (EEG)-Based Brain–Computer Interface (BCI): A 2-D Virtual Wheelchair Control Based on Event-Related Desynchronization/Synchronization and State Control
TL;DR: The superior control performance in subjects without intensive BCI training suggested a practical wheelchair control paradigm for BCI users and expected users to experience ease of control by including a noncontrol state.
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Determination of awareness in patients with severe brain injury using EEG power spectral analysis
TL;DR: Electro EEG power spectral analysis can be used as a flexible bedside tool to demonstrate awareness in brain-injured patients who are otherwise unable to communicate.
References
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EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.
Arnaud Delorme,Scott Makeig +1 more
TL;DR: EELAB as mentioned in this paper is a toolbox and graphic user interface for processing collections of single-trial and/or averaged EEG data of any number of channels, including EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), Independent Component Analysis (ICA) and time/frequency decomposition including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling.
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Ten lectures on wavelets
TL;DR: This paper presents a meta-analyses of the wavelet transforms of Coxeter’s inequality and its applications to multiresolutional analysis and orthonormal bases.
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Ten Lectures on Wavelets
TL;DR: In this article, the regularity of compactly supported wavelets and symmetry of wavelet bases are discussed. But the authors focus on the orthonormal bases of wavelets, rather than the continuous wavelet transform.