Brain Computer Interface / BCI / Neural Control Int. / NCI / Mind Machine Int. / MMI / Direct Neural Int. / DNI / Brain Machine Int. / BMI
Marzie Alirezaei Alavijeh; Ali Maleki
Volume 16, Issue 1 , May 2022, , Pages 1-9
Abstract
Nowadays, brain-computer interface system based on steady-state visual evoked potentials is increased due to advantages such as accepted accuracy and minimal need for user training. Despite these benefits, the unwanted noise that affects SSVEP is one of the issues that can reduce the efficiency of such ...
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Nowadays, brain-computer interface system based on steady-state visual evoked potentials is increased due to advantages such as accepted accuracy and minimal need for user training. Despite these benefits, the unwanted noise that affects SSVEP is one of the issues that can reduce the efficiency of such systems. This paper uses the EMD algorithm in the initial phase and CCA or LASSO for the recognition of the stimulation frequency. In the first step, the EMD algorithm is applied so that non-stationary SSVEP signal breaks into oscillating functions and meaningful information are extracted. Among the IMFs obtained from the EMD method, only IMFs whose amplitude of the frequency spectrum in the frequency ranges corresponding to the excitation is higher were selected. With this selection, noisy signals and unprofitable information can be omitted. In the proposed method, two CCA and LASSO diagnostic methods were performed on the sum of selected signals to identify the frequency of stimulation. The simulation results show the recognition accuracy of 81.76% and 82.26% for the proposed method EMD-CCA and EMD-LASSO, respectively. While detection accuracy is 78.10% and 78.72% for conventional methods of CCA and LASSO.
Seyedeh Saeideh Zahedi Haghighi; Sayed Mahmoud Sakhaei; Mohammadreza Daliri
Volume 13, Issue 2 , August 2019, , Pages 95-104
Abstract
Emotion is one of the most important human quality that plays an important role in life. Also, one way of communicating between human and computer is based on emotion recognition. One way of emotion recognition is based on electroencephalographic signal (EEG). In this paper, according to the non-stationary ...
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Emotion is one of the most important human quality that plays an important role in life. Also, one way of communicating between human and computer is based on emotion recognition. One way of emotion recognition is based on electroencephalographic signal (EEG). In this paper, according to the non-stationary property of EEG, intrinsic mode functions (IMF) extracted by using empirical mode decomposition (EMD) and then first 3 IMFs selected. Each IMF converts into smaller pieces with a one-second window and power feature has been extracted from each piece. Then, by using a suitable mapping, the position of the electrodes in the 10-20 system becomes the position of the pixels in the picture. The extracted features are considered as pixel color components. To determine the class of valence, the set of all generated pictures is given as input to a deep learning network and output determine the high or low class of valence. The same process is used to determine the class of arousal. To examining the method, the DEAP dataset is used. By choosing 17×17 for the image size, the mean accuracy and standard deviation were obtained of 78.58% and 3.9 for the valence and 78.66% and 3.1 for the arousal which that shows a significant improvement compared to similar tasks.
Human Computer Interaction / HCI
Sahar Sadeghi; Ali Maleki
Volume 11, Issue 2 , June 2017, , Pages 101-109
Abstract
To increase the number of stimulation frequencies in the Steady-state visual evoked potential (SSVEP)-based brain-computer interface, we are forced to broaden the frequency range due to the frequency resolution restriction. This will enter frequencies with harmonic relation into the stimulation frequency ...
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To increase the number of stimulation frequencies in the Steady-state visual evoked potential (SSVEP)-based brain-computer interface, we are forced to broaden the frequency range due to the frequency resolution restriction. This will enter frequencies with harmonic relation into the stimulation frequency range and lead to increase in frequency recognition error. In this paper, a three-stage method including the empirical mode decomposition (EMD), the canonical correlation analysis (CCA) and neural network classifier has been proposed that can solve the recognition error problem for wide frequency range including frequencies with harmonic relation. Visual stimulus ranged from 6-16 Hz with an interval of 0.5 have been generated using Matlab and the psychophysics toolbox. The SSVEP signal was recorded from ten subjects via one electrode placed at Oz. After extracting the intrinsic mode functions (IMFs) of the signal by EMD and reconstructing the combined signals, the CCA has been applied. Two features including the detected frequency and the correlation value in this frequency have been extracted and they were given to the neural network classifier. For eight-second time window, the average accuracy of the CCA for N=1 was 78% and for N=2 was 74%, while the corresponding values of the proposed method were 82% and 77% respectively. For four-second time window, the accuracy was increased from 78% to 83% for N=1 and it was increased from 78% to 80% for N=2. N is the number of harmonics in the generation of the reference signal in the CCA. For wide frequency range, the proposed method has been able to improve the frequency recognition accuracy compared to the standard CCA method. according to this, by broadening the stimulation frequency range, the possibility of increasing the number of frequency options and thus increasing the information transfer rate are provided.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mohsen Naji; Seyed Mohammad Firouzabadi; Sedighe Kahrizi
Volume 7, Issue 1 , June 2013, , Pages 13-20
Abstract
The collected electromyogram (EMG) signals from trunk musculature (e.g., rectus abdominis and external oblique muscle) are often contaminated with the heart muscle electrical activity (ECG). This paper introduces a novel method, the Empirical Mode Decomposition, for elimination of ECG contamination from ...
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The collected electromyogram (EMG) signals from trunk musculature (e.g., rectus abdominis and external oblique muscle) are often contaminated with the heart muscle electrical activity (ECG). This paper introduces a novel method, the Empirical Mode Decomposition, for elimination of ECG contamination from EMG signals. The method is compared to a Butterworth high pass filtering. Results obtained from the analysis of generated and experimental EMG signals show that our method outperforms the high pass filtering for elimination of ECG contamination from trunk EMG signals.
