A self-paced brain–computer interface for controlling a robot simulator: an online event labelling paradigm and an extended Kalman filter based algorithm for online training

Tsui, Chun Sing Louis; Gan, John Q.; Roberts, Stephen

doi:10.1007/s11517-009-0459-7

Cited by 73 publications

(44 citation statements)

References 21 publications

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“…For frequency filtering, we applied a filter-bank with two blocks in ranges [8][9][10][11][12]Hz and [16][17][18][19][20][21][22][23][24]Hz corresponding to the typical alpha and beta frequencies of the brain. For spatial filtering, we tried CSP with two, four and six filters.…”

Section: Resultsmentioning

confidence: 99%

“…Thus any classical supervised learning method can be used to solve the problem. The majority of the publications in the field have used this method to build different self-paced BCIs [8], [9], [10]. The disadvantage of the sliding window approach is that the sequential correlation of the labels of consecutive EEG windows is not exploited.…”

Section: H Bashashati Et Al / Advances In Science Technology and Ementioning

confidence: 99%

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Ensemble of Neural Network Conditional Random Fields for Self-Paced Brain Computer Interfaces

Bashashati¹,

Ward²

2017

Adv. sci. technol. eng. syst. j.

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Section: Resultsmentioning

confidence: 99%

Section: H Bashashati Et Al / Advances In Science Technology and Ementioning

confidence: 99%

Ensemble of Neural Network Conditional Random Fields for Self-Paced Brain Computer Interfaces

Bashashati¹,

Ward²

2017

Adv. sci. technol. eng. syst. j.

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“…The use of this rather small number of electrodes has proven successful in our previous work [11] , in spite of suggestions that a higher quantity of electrodes could facilitate classification [6,12] .…”

Section: Description Of the Data And The Power Spectrum Densitymentioning

confidence: 92%

Anomalous pattern based clustering of mental tasks with subject independent learning – some preliminary results

Amorim

Mirkin²,

Gan³

2012

AIR

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In this paper we describe a new method for EEG signal classification in which the classification of one subject's EEG signals is based on features learnt from another subject. This method applies to the power spectrum density data and assigns class-dependent information weights to individual features. The informative features appear to be rather similar among different subjects, thus supporting the view that there are subject independent general brain patterns for the same mental task.Classification is done via clustering using the intelligent k-means algorithm with the most informative features from a different subject. We experimentally compare our method with others.

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“…72.55 % [12,8] 66.86 % P4 54.06 % [9,8] 64.08 % [10,3] 48.90 % [8,7] 68.72 % [11,5] 58.94 % P5 49.51 % [8,6] 49.48 % [8,6] 61.73 % [10,6] 54.87 % [9,7] 53.90 %…”

Section: % [12 2]unclassified

A novel onset detection technique for brain–computer interfaces using sound-production related cognitive tasks in simulated-online system

Song

Sepúlveda²

2017

J. Neural Eng.

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Objective. Self-paced EEG-based BCIs (spBCIs) have traditionally been avoided due to two sources of uncertainty: 1) precisely when an intentional command is sent by the brain, i.e., the command onset detection problem, and 2) how different the intentional command is when compared to non-specific (or idle) states. Performance evaluation is also a problem and there are no suitable standard metrics available. In this paper we attempted to tackle these issues.Approach. Self-paced covert sound-production cognitive tasks (i.e., high pitch and siren-like sounds) were used to distinguish between intentional commands (IC) and idle states. The IC states were chosen for their ease of execution and negligible overlap with common cognitive states. Band power and a digital wavelet transform were used for feature extraction, and the Davies-Bouldin index was used for feature selection. Classification was performed using LDA.Main results. Performance was evaluated under offline and simulated-online conditions. For the latter, a performance score called true-false-positive (TFP) rate, ranging from 0 (poor) to 100 (perfect), was created to take into account both classification performance and onset timing errors. Averaging the results from the best performing IC task for all seven participants, an 77.7% true-positive rate was achieved in offline testing. For simulated-online analysis the best IC average TFP score was 76.67% (87.61% true-positive rate, 4.05% false-positive rate).Significance. Results were promising when compared to previous IC onset detection studies using motor imagery, in which best true-positive rates were reported as 72.0% and 79.7%, and which, crucially, did not take timing errors into account. Moreover, based on our literature review, there is no previous covert sound-production onset detection system for spBCIs. Results showed that the proposed onset detection technique and TFP performance metric have good potential for use in spBCIs.

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A self-paced brain–computer interface for controlling a robot simulator: an online event labelling paradigm and an extended Kalman filter based algorithm for online training

Cited by 73 publications

References 21 publications

Ensemble of Neural Network Conditional Random Fields for Self-Paced Brain Computer Interfaces

Ensemble of Neural Network Conditional Random Fields for Self-Paced Brain Computer Interfaces

Anomalous pattern based clustering of mental tasks with subject independent learning – some preliminary results

A novel onset detection technique for brain–computer interfaces using sound-production related cognitive tasks in simulated-online system

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