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Preprocessing by a Bayesian single-trial event-related potential estimation technique allows feasibility of an assistive single-channel P300-based brain-computer interface.

Goljahani A, D'Avanzo C, Silvoni S, Tonin P, Piccione F, Sparacino G - Comput Math Methods Med (2014)

Bottom Line: A major clinical goal of brain-computer interfaces (BCIs) is to allow severely paralyzed patients to communicate their needs and thoughts during their everyday lives.Among others, P300-based BCIs, which resort to EEG measurements, have been successfully operated by people with severe neuromuscular disabilities.Besides reducing the number of stimuli repetitions needed to detect the P300, a current challenge in P300-based BCI research is the simplification of system's setup and maintenance by lowering the number N of recording channels.

View Article: PubMed Central - PubMed

Affiliation: Department of Information Engineering, University of Padova, Via Gradenigo 6/B, 35131 Padova, Italy.

ABSTRACT
A major clinical goal of brain-computer interfaces (BCIs) is to allow severely paralyzed patients to communicate their needs and thoughts during their everyday lives. Among others, P300-based BCIs, which resort to EEG measurements, have been successfully operated by people with severe neuromuscular disabilities. Besides reducing the number of stimuli repetitions needed to detect the P300, a current challenge in P300-based BCI research is the simplification of system's setup and maintenance by lowering the number N of recording channels. By using offline data collected in 30 subjects (21 amyotrophic lateral sclerosis patients and 9 controls) through a clinical BCI with N = 5 channels, in the present paper we show that a preprocessing approach based on a Bayesian single-trial ERP estimation technique allows reducing N to 1 without affecting the system's accuracy. The potentially great benefit for the practical usability of BCI devices (including patient acceptance) that would be given by the reduction of the number N of channels encourages further development of the present study, for example, in an online setting.

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Functional blocks of the reference MC BCI and of the simulated SC prototype. MC (solid lines) and SC (dashed lines) preprocessing blocks are highlighted on the left side.
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fig2: Functional blocks of the reference MC BCI and of the simulated SC prototype. MC (solid lines) and SC (dashed lines) preprocessing blocks are highlighted on the left side.

Mentions: EEG raw epochs, starting 500 ms before and ending 1000 ms after each flashing, were extracted and baseline was corrected to the mean of prestimuli data, resulting in npre = 100 prestimulus and npost = 200 poststimulus samples. Functional blocks of the reference multichannel (MC) BCI [13] are graphically illustrated in Figure 2. Briefly, in correspondence with each flashing, raw epochs from N = 5 channels, that is, Fz, Cz, Pz, Oz, and EOG, fed a single-trial ICA decomposition block that produced five independent components. One of the components was, then, selected and used to extract the information (features) supplied to the classifier to take the decision about whether the stimulus that produced the signal was target or nontarget. Seventy-eight features were computed for each epoch and comprised, for example, latencies and values of minimum and maximum peaks, power of the signal in 200 ms windows, and wavelet coefficients [13, 17]. Feature vectors were classified by means of a support vector machine (SVM) classifier with a radial basis function kernel [41].


Preprocessing by a Bayesian single-trial event-related potential estimation technique allows feasibility of an assistive single-channel P300-based brain-computer interface.

Goljahani A, D'Avanzo C, Silvoni S, Tonin P, Piccione F, Sparacino G - Comput Math Methods Med (2014)

Functional blocks of the reference MC BCI and of the simulated SC prototype. MC (solid lines) and SC (dashed lines) preprocessing blocks are highlighted on the left side.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4109663&req=5

fig2: Functional blocks of the reference MC BCI and of the simulated SC prototype. MC (solid lines) and SC (dashed lines) preprocessing blocks are highlighted on the left side.
Mentions: EEG raw epochs, starting 500 ms before and ending 1000 ms after each flashing, were extracted and baseline was corrected to the mean of prestimuli data, resulting in npre = 100 prestimulus and npost = 200 poststimulus samples. Functional blocks of the reference multichannel (MC) BCI [13] are graphically illustrated in Figure 2. Briefly, in correspondence with each flashing, raw epochs from N = 5 channels, that is, Fz, Cz, Pz, Oz, and EOG, fed a single-trial ICA decomposition block that produced five independent components. One of the components was, then, selected and used to extract the information (features) supplied to the classifier to take the decision about whether the stimulus that produced the signal was target or nontarget. Seventy-eight features were computed for each epoch and comprised, for example, latencies and values of minimum and maximum peaks, power of the signal in 200 ms windows, and wavelet coefficients [13, 17]. Feature vectors were classified by means of a support vector machine (SVM) classifier with a radial basis function kernel [41].

Bottom Line: A major clinical goal of brain-computer interfaces (BCIs) is to allow severely paralyzed patients to communicate their needs and thoughts during their everyday lives.Among others, P300-based BCIs, which resort to EEG measurements, have been successfully operated by people with severe neuromuscular disabilities.Besides reducing the number of stimuli repetitions needed to detect the P300, a current challenge in P300-based BCI research is the simplification of system's setup and maintenance by lowering the number N of recording channels.

View Article: PubMed Central - PubMed

Affiliation: Department of Information Engineering, University of Padova, Via Gradenigo 6/B, 35131 Padova, Italy.

ABSTRACT
A major clinical goal of brain-computer interfaces (BCIs) is to allow severely paralyzed patients to communicate their needs and thoughts during their everyday lives. Among others, P300-based BCIs, which resort to EEG measurements, have been successfully operated by people with severe neuromuscular disabilities. Besides reducing the number of stimuli repetitions needed to detect the P300, a current challenge in P300-based BCI research is the simplification of system's setup and maintenance by lowering the number N of recording channels. By using offline data collected in 30 subjects (21 amyotrophic lateral sclerosis patients and 9 controls) through a clinical BCI with N = 5 channels, in the present paper we show that a preprocessing approach based on a Bayesian single-trial ERP estimation technique allows reducing N to 1 without affecting the system's accuracy. The potentially great benefit for the practical usability of BCI devices (including patient acceptance) that would be given by the reduction of the number N of channels encourages further development of the present study, for example, in an online setting.

Show MeSH
Related in: MedlinePlus