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This paper presents a comparison between two different technologies of acquisition systems (BrainNet36 and Emotiv Epoc) for an Independent-BCI based on Steady-State Visual Evoked Potential (SSVEP). Two stimuli separated by a viewing angle < 1° were used. Multivariate Synchronization Index (MSI) technique was used as feature extractor and five subjects participated in the experiments. The class...
This paper presents the comparation of three different feature extraction techniques based on the Empirical Mode Decomposition (EMD) for a SSVEP-BCI. This approach based on the characterization of the signal by EMD, is proposed as a novel alternative to other techniques and it was demonstrated that it exceeds both in accuracy rate and Information Transfer Rate (ITR). The experiments were performed...
This paper presents the evaluation of seven techniques of feature extraction (PSD, F-Test, EMD, MCE, CCA, LASSO and MSI) for gaze-target detections in a SSVEP-based BCI. Two type of technologies for visual stimulation were used (LCD and LEDs). Five differents windows lengths (1, 2, 4, 5 and 10 s) were used and seven volunteers participated in this study. The highest accuracy obtained in all cases...
This paper presents the classification of motor tasks, using surface electromyography (sEMG) to control a virtual prosthetic hand for rehabilitation of amputees. Two types of classifiers are compared: k-Nearest Neighbor (k-NN) and Bayesian (Discriminant Analysis). Motor tasks are divided into four groups correlated. The volunteers were people without amputation and several analyzes of each of the...
This paper presents the classification of motor tasks, using surface electromyography (sEMG) to control a prosthetic hand for rehabilitation of amputees. Two types of classifiers are compared: k-Nearest Neighbor (k-NN) and Bayesian (Discriminant Analysis). Motor tasks are divided into four groups correlated. The volunteers were healthy people (without amputation) and several analyzes of each of the...
In this study, we present a real-time system to control a virtual hand using traditional features of surface electromyography (sEMG). The sEMG signal was recorded while performing simple finger and wrist movements related to the day-to-day activities. Traditional features of sEMG: RMS (Root Mean Square), VAR (Variance) and WL (Waveform Length) were computed using the sliding window technique. These...
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