BCI Competition III: Dataset II- Ensemble of SVMs for BCI P300 Speller

Rakotomamonjy, Alain; Guigue, Vincent

doi:10.1109/tbme.2008.915728

Cited by 436 publications

(353 citation statements)

References 13 publications

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“…We borrowed from the work of Rakotomamonjy and Guigue [21] which resulted winner of the III BCI Competition for the Donchin speller. Namely, we used the approach they called "Ensemble SVM without channel selection" because it is easier to implement and outperforms other alternatives when using only 5 sequences to classify a character.…”

Section: A Methodsmentioning

confidence: 99%

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Exploiting P300 amplitude variations can improve classification accuracy in Donchin's BCI speller

Citi

Poli

Cinel

2009

2009 4th International IEEE/EMBS Conference on Neural Engineering

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Abstract-The P300 is an endogenous component of EEG event related potentials which is elicited by rare and significant stimuli. P300s are used increasingly frequently in Brain Computer Interfaces (BCI) because, being naturally elicited in response to external stimuli, users do not need special training. However, P300 waves are hard to detect and, therefore, multiple stimulus presentations are needed before an interface can make a reliable decision. While significant improvements have been made in the detection of P300s, no particular attention has been paid to the variability in shape and timing of P300 waves and its exploitation in BCI. In this paper we start filling this gap, by first documenting and then exploiting a modulation in amplitude of P300 caused by target-to-target interval (TTI) differences. We demonstrate this within the context of the Donchin's speller, which is perhaps the best known example of a BCI system relying on the detection P300 waves, where target-to-target interval variations are induced by stimuli randomisation. In particular we show that by specialising detectors to work with P300s elicited with each TTI, we can further improve the performance of the best known Donchin's speller with minimal changes.

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Section: A Methodsmentioning

confidence: 99%

“…A stochastic hill climber was (1) and (2)). For the two subjects, the performance of the approach introduced in this paper ("expl P300 var") is compared to the reference algorithm ("ref RG") by Rakotomamonjy and Guigue [21].…”

Section: A Methodsmentioning

confidence: 99%

Exploiting P300 amplitude variations can improve classification accuracy in Donchin's BCI speller

Citi

Poli

Cinel

2009

2009 4th International IEEE/EMBS Conference on Neural Engineering

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“…In Rakotomamonjy's method, each classifier is composed of a linear Support Vector Machine (SVM) trained on a small part of the available data (Rakotomamonjy and Guigue 2008). They grouped consecutive EEG trials in a short term and used them to train unit classifiers.…”

Section: A Brain-computer Interface (Bci) Is a Communication Ormentioning

confidence: 99%

Improving N1 classification by grouping EEG trials with phases of pre-stimulus EEG oscillations

Han

Zhang

et al. 2014

Cogn Neurodyn

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A reactive brain-computer interface using electroencephalography (EEG) relies on the classification of evoked ERP responses. As the trial-to-trial variation is evitable in EEG signals, it is a challenge to capture the consistent classification features distribution. Clustering EEG trials with similar features and utilizing a specific classifier adjusted to each cluster can improve EEG classification. In this paper, instead of measuring the similarity of ERP features, the brain states during image stimuli presentation that evoked N1 responses were used to group EEG trials. The correlation between momentary phases of pre-stimulus EEG oscillations and N1 amplitudes was analyzed. The results demonstrated that the phases of timefrequency points about 5.3 Hz and 0.3 s before the stimulus onset have significant effect on the ERP classification accuracy. Our findings revealed that N1 components in ERP fluctuated with momentary phases of EEG. We also further studied the influence of pre-stimulus momentary phases on classification of N1 features. Results showed that linear classifiers demonstrated outstanding classification performance when training and testing trials have close momentary phases. Therefore, this gave us a new direction to improve EEG classification by grouping EEG trials with similar pre-stimulus phases and using each to train unit classifiers respectively.

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“…It is obvious that the number of samples is large when EEG is recorded with a low sampling frequency (e.g., 240 Hz) and a few channels (e.g., 11 channels). The most common technique to reduce the dimensionality of P300 waveform is to downsample the signals after applying a moving average filter [5,11]. The downsampled data then can be used as features for classification.…”

Section: Feature Extraction For P300-based Bcimentioning

confidence: 99%

“…We note that spatial information also affects the P300 detection. For example, we can select dominant channels by a recursive elimination [11].…”

Section: Feature Extraction For P300-based Bcimentioning

confidence: 99%

Tensor classification for P300-based brain computer interface

Onishi

Phan

Matsuoka

et al. 2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Classification methods have been widely applied in most brain computer interfaces (BCIs) that control devices for better quality of life. Most existing classification methods for P300-based BCIs extract features based on temporal structure related to P300 components of event-related potentials (ERPs). Some others exploit the spatial distribution of ERPs optimally selected by recursive channel elimination. However, none of them employed multilinear structures which exploit hidden features in P300-based BCI data. In this paper, we propose a new feature extraction method based on tensor decomposition for ERP-based BCIs. The method seeks an optimal feature subspace simultaneously spanned by temporal and spatial bases, and additional bases which indicate a variant of ERPs obtained by different degrees of polynomial fittings. The proposed method has been evaluated by both the BCI competition III data set II and the affective face driven paradigm data set, and achieved 92% and 95% classification accuracies respectively, which were better than those of most existing P300-based BCI algorithms.Index Terms-Brain-Computer Interface (BCI), P300-based BCI, facial image, tensor, higher order discriminant analysis (HODA), electroencephalography (EEG), event-related potentials (ERPs).

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BCI Competition III: Dataset II- Ensemble of SVMs for BCI P300 Speller

Cited by 436 publications

References 13 publications

Exploiting P300 amplitude variations can improve classification accuracy in Donchin's BCI speller

Exploiting P300 amplitude variations can improve classification accuracy in Donchin's BCI speller

Improving N1 classification by grouping EEG trials with phases of pre-stimulus EEG oscillations

Tensor classification for P300-based brain computer interface

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