Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported

Multiclass Posterior Probability Twin SVM for Motor Imagery EEG Classification

Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve this problem, a multiclass posterior probability solution for twin SVM is proposed by the ranking continuous output and pairwise coupling in this paper. First, two-class posterior probability model is constructed to approximate the posterior probability by the ranking continuous output techniques and Platt's estimating method. Secondly, a solution of multiclass probabilistic outputs for twin SVM is provided by combining every pair of class probabilities according to the method of pairwise coupling. Finally, the proposed method is compared with multiclass SVM and twin SVM via voting, and multiclass posterior probability SVM using different coupling approaches. The efficacy on the classification accuracy and time complexity of the proposed method has been demonstrated by both the UCI benchmark datasets and real world EEG data from BCI Competition IV Dataset 2a, respectively

Analisis Keunikan Fitur Cwt Sinyal Eeg Untuk Pembuatan Lima Indikator Pengendalian Kursi Roda BCI

Penelitian ini dilakukan dengan tujuan untuk membuat lima indikator pengendalian kursi roda BCI berdasarkan fitur yang diekstraksi dari sinyal elektroensefalogram (EEG). Sinyal EEG didekomposisi menggunakan metode continuous wavelet transform (CWT). Nilai rata-rata absolut dan standar deviasi dari sinyal yang telah didekomposisi tersebut digunakan sebagai fitur. Fitur hasil ekstraksi kemudian dianalisis keunikannya menggunakan metode Friedman. Untuk mendekati sifat alami fitur sinyal EEG yang nonlinier, metode support vector machine (SVM) dengan kernel radial basis function (RBF) digunakan untuk membuat indikator pengendalian kursi roda BCI berdasarkan fitur sinyal EEG yang paling unik. Hasil penelitian ini menunjukkan bahwa metode yang diusulkan dapat mengukur tingkat keunikan fitur CWT sinyal EEG. Dari penelitian penentuan keunikan fitur CWT dapat diperoleh lima indikator pengendalian untuk kursi roda BCI yang didasarkan pada sinyal EEG dari Neurosky MW001. Akan tetapi, akurasi kelima indikator tersebut belum dapat digunakan sebagai indikator kontrol untuk aktuator kursi roda BCI. Hal ini disebabkan oleh tingkat kepercayaan rata-rata indikator tersebut masih di bawah 60%, sedangkan untuk indikator yang berpasangan masih di bawah 70%