Brain-computer interfaces (BCIs) have been gaining momentum in making human-computer interaction more natural, especially for people with neuro-muscular disabilities. Among the existing solutions the systems relying on electroencephalograms (EEG) occupy the most prominent place due to their non-invasiveness. However, the process of translating EEG signals into computer commands is far from trivial, since it requires the optimization of many different parameters that need to be tuned jointly. In this report, we focus on the category of EEG-based BCIs that rely on Steady-State-Visual-Evoked Potentials (SSVEPs) and perform a comparative evaluation of the most promising algorithms existing in the literature. More specifically, we define a set of algorithms for each of the various different parameters composing a BCI system (i.e. filtering, artifact removal, feature extraction, feature selection and classification) and study each parameter independently by keeping all other parameters fixed. The results obtained from this evaluation process are provided together with a dataset consisting of the 256-channel, EEG signals of 11 subjects, as well as a processing toolbox for reproducing the results and supporting further experimentation. In this way, we manage to make available for the community a state-of-the-art baseline for SSVEP-based BCIs that can be used as a basis for introducing novel methods and approaches.
The existence of various options for the implementation of each submodule has motivated a non-trivial number of comparative studies for BCI systems that have been reported in the literature. In @cite_12 a comparison study was presented with respect to the classification technique. However, the comparison was limited between SVM and NN. Furthermore, in the feature extraction stage only features produced by FFT are used. In @cite_39 a more exhaustive comparative study has been presented. More specifically, in the feature extraction stage three different data sets have been produced based on spectral analysis, filterbank theory and time - frequency domain. In addition in the feature selection stage, three feature selection approaches are used, two filters, the Pearson's filter and the Davies Bouldin (DB) index, and one wrapper algorithm. A more thorough comparative study is presented in @cite_45 with respect to BCI systems. In this work the study was concentrated around numerous classification algorithms and the application of them in various BCI systems.
@cite_45 @cite_12 @cite_39
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In this paper we review classification algorithms used to design brain–computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI. In recent years, Brain Computer Interface (BCI) systems based on Steady-State Visual Evoked Potential (SSVEP) have received much attentions. This study tries to develop a classifier, which can provide higher classification accuracy for multiclass SSVEP data. Four different flickering frequencies in low frequency region were used to elicit the SSVEPs and were displayed on a Liquid Crystal Display (LCD) monitor using LabVIEW. The Electroencephalogram (EEG) signals recorded from the occipital region were first segmented into 1 second window and features were extracted using Fast Fourier Transform (FFT). One-Against-All (OAA), a popular strategy for multiclass Support Vector Machines (SVM) is compared with Artificial Neural Network (ANN) models on the basis of SSVEP classifier accuracies. OAA SVM classifier had got an average accuracy of 88.55 for SSVEP classification over 10 subjects. Based on this study, it is found that for SSVEP classification OAA -SVM classifier can provide better results