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Abstract
The brain is a large-scale complex network often referred to as the “connectome”. Cognitive functions and information processing are mainly based on the interactions between distant brain regions. However, most of the ‘feature extraction’ methods used in the context of Brain Computer Interface (BCI) ignored the possible functional relationships between different signals recorded from distinct brain areas. In this paper, the functional connectivity quantified by the phase locking value (PLV) was introduced to characterize the evoked responses (ERPs) obtained in the case of target and non-targets visual stimuli. We also tested the possibility of using the functional connectivity in the context of ‘P300 speller’. The proposed approach was compared to the well-known methods proposed in the state of the art of “P300 Speller”, mainly the peak picking, the area, time/frequency based features, the xDAWN spatial filtering and the stepwise linear discriminant analysis (SWLDA). The electroencephalographic (EEG) signals recorded from ten subjects were analyzed offline. The results indicated that phase synchrony offers relevant information for the classification in a P300 speller. High synchronization between the brain regions was clearly observed during target trials, although no significant synchronization was detected for a non-target trial. The results showed also that phase synchrony provides higher performance than some existing methods for letter classification in a P300 speller principally when large number of trials is available. Finally, we tested the possible combination of both approaches (classical features and phase synchrony). Our findings showed an overall improvement of the performance of the P300-speller when using Peak picking, the area and frequency based features. Similar performances were obtained compared to xDAWN and SWLDA when using large number of trials.
Highlights
A Brain Computer Interface (BCI) is a communication system in which messages or commands that an individual sends to the external world do not pass through the brain’s normalPLOS ONE | DOI:10.1371/journal.pone.0146282 January 11, 2016P300-Speller and Brain Functional Connectivity output pathways of peripheral nerves and muscles [1]
In order to evaluate the classification performance of each of the algorithm, the following steps were realized: (a) A feature vector was built from the large set of Ntotal evoked responses (ERPs) signals generated from the four raw EEG signals of each subject, (b) Data were divided into training/testing sets using the cross-validation procedure to prevent over-fitting, (c) The classifier was trained using the training data set(d) The classifier was applied to the rest of the Ntest characters and the accuracy values were calculated, the (b)-(c)-(d) processes were repeated 10 times, and (e) the accuracy was defined as the average of the accuracies calculated over the repetitions
After the comparison with surrogates data, a large number of edges were remained in the connected topographical map in the case of target trials, while a very few connected edges were conserved in the case of non-target trials
Summary
A Brain Computer Interface (BCI) is a communication system in which messages or commands that an individual sends to the external world do not pass through the brain’s normalPLOS ONE | DOI:10.1371/journal.pone.0146282 January 11, 2016P300-Speller and Brain Functional Connectivity output pathways of peripheral nerves and muscles [1]. One of the most popular non-invasive BCIs is the so called 'P300 speller' that helps people suffering from severe neuromuscular disorders like amyotrophic lateral sclerosis (ALS), multiple sclerosis, cerebral palsy, spinal cord injuries and stroke. These disorders usually result in loss of voluntary muscle control due to the destruction of motor neurons with intact cognitive abilities [2][3]. The second step involves the pre-processing of the recorded data. This step is essentially realized to remove artifacts or non-useful information. More advanced techniques have been reported including stepwise linear discriminant analysis (SWLDA) [11][7], support vector machines [12][13][14], spatial filtering using xDAWN algorithm [15] and information geometry framework [16]
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