Título: Detector De Coerência E Modelos Ocultos De Markov Aplicados À Classificação De Tarefas De Imagética Motora
Autores: Souza, Ana Paula; Santos Filho, Sady Antonio dos; Tierra-Criollo, Carlos Julio
Resumo: The most investigated stages in Brain–Computer Interface are features extraction and task classification. Thus, this work investigates the application of the coherence detector (Magnitude Squared Coherence – MSC) and the Hidden Markov Models (HMM) in the extraction of features and tasks classification, respectively. Features were extracted from electroencephalogram (EEG) in the Delta band (0.1–2 Hz), Alpha band (8–13 Hz) and Beta band (14–30 Hz) using coherence with 5% and 10% significance level (α). The EEG signals were recorded from three healthy subjects during three events: spontaneous EEG, EEG-based motor task and EEG-based motor imagination. We recorded EEG with electrodes placed according to the international 10–20 (first section) and 10–10 systems (second and third sections). HMM observations were obtained by the coherence calculated with 12 trials and the detected frequency range with higher MSC was adopted as a feature for classifier. The hit rate in classification was 72.5 %, 68 % and 65 % for subjects # 1, # 2 and # 3, respectively, using α=5 %. When we used α=10 %, the rates were 72 %, 57.5 % and 67.5 %. Results shown we can extract features from brain activities related to different events by using coherence detector and that HMM is useful in the classification of imaginary movements.
Palavras-chave: Brain Machine Interface; EEG; HMM; Coherence Detector; Real Movement; Movement Imagination
Código DOI: 10.21528/CBIC2011-13.4
Artigo em pdf: st_13.4.pdf
Arquivo BibTex: st_13.4.bib