3,669 research outputs found
Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review
Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported
The Impact of Flow in an EEG-based Brain Computer Interface
Major issues in Brain Computer Interfaces (BCIs) include low usability and
poor user performance. This paper tackles them by ensuring the users to be in a
state of immersion, control and motivation, called state of flow. Indeed, in
various disciplines, being in the state of flow was shown to improve
performances and learning. Hence, we intended to draw BCI users in a flow state
to improve both their subjective experience and their performances. In a Motor
Imagery BCI game, we manipulated flow in two ways: 1) by adapting the task
difficulty and 2) by using background music. Results showed that the difficulty
adaptation induced a higher flow state, however music had no effect. There was
a positive correlation between subjective flow scores and offline performance,
although the flow factors had no effect (adaptation) or negative effect (music)
on online performance. Overall, favouring the flow state seems a promising
approach for enhancing users' satisfaction, although its complexity requires
more thorough investigations
Integrating EEG and MEG signals to improve motor imagery classification in brain-computer interfaces
We propose a fusion approach that combines features from simultaneously
recorded electroencephalographic (EEG) and magnetoencephalographic (MEG)
signals to improve classification performances in motor imagery-based
brain-computer interfaces (BCIs). We applied our approach to a group of 15
healthy subjects and found a significant classification performance enhancement
as compared to standard single-modality approaches in the alpha and beta bands.
Taken together, our findings demonstrate the advantage of considering
multimodal approaches as complementary tools for improving the impact of
non-invasive BCIs
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