Brain Computer Interface / BCI / Neural Control Int. / NCI / Mind Machine Int. / MMI / Direct Neural Int. / DNI / Brain Machine Int. / BMI
Ali Maleki; Maedeh Azadimoghadam
Volume 16, Issue 3 , December 2022, , Pages 229-240
Abstract
A significant challenge in moving SSVEP-based BCIs from the laboratory into real-life applications is that the user may suffer from fatigue. Prolonged execution of commands in a BCI system can cause mental fatigue and, as a result, create dissatisfaction in the user and reduce the system's efficiency. ...
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A significant challenge in moving SSVEP-based BCIs from the laboratory into real-life applications is that the user may suffer from fatigue. Prolonged execution of commands in a BCI system can cause mental fatigue and, as a result, create dissatisfaction in the user and reduce the system's efficiency. The first step to studying and ultimately reducing the destructive effects of fatigue is to identify the level of fatigue. Although frequency indices have been used for fatigue evaluation, the results of previous research in this field are inconsistent. Therefore, there is no detailed and comprehensive investigation of how fatigue affects frequency indices. In this paper, the evaluation of frequency-domain fatigue indicators has been done accurately and comprehensively. For this purpose, nine visual stimuli with different flickering frequencies were displayed to the subject, and they were asked to pay attention to the target cue. The visual stimulation was presented continuously, without rest to ensure that the fatigue occurs at the end of the test. Mean amplitude of theta, alpha, and beta bands, and 4-30Hz frequency band segments with 1Hz, 2Hz, and 4Hz steps were evaluated as fatigue indices. The results show that the mean amplitude of the frequency band of 8-9 Hz is more suitable for fatigue evaluation. This index has the most changes with fatigue in a state of wakeful relaxation of the subject and the mental effort to maintain the level of alertness in the fatigue state.
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.
Neuro-Muscular Engineering
Tahmineh Sadati; Mohammad Reza Daliri
Volume 12, Issue 1 , June 2018, , Pages 1-10
Abstract
A brain-computer interface is a system which works based on the neural activity created by the brain and it has attracted the attention of many researchers in recent years. These interfaces are independent of the usual pathway of peripheral and muscular nerves and are very important because of their ...
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A brain-computer interface is a system which works based on the neural activity created by the brain and it has attracted the attention of many researchers in recent years. These interfaces are independent of the usual pathway of peripheral and muscular nerves and are very important because of their ability to provide a new dimension in communication or control of a device for the disabled persons. The neural activity used in the brain-computer interface can be recorded by various invasive and non-invasive methods and can be converted to the desired signal by different decoding algorithms. In this study, 3 rats were used to perform a movement task which was pressing a key and receiving a drop of water by a mechanical arm for corrected trials. By implanting a 16-channel microelectrode array in the rat's motor cortex during an invasive process, the brain signals are recorded during the task, and simultaneously the signal received by the force sensor is also stored. By performing the necessary preprocessing on spikes and extracting the firing rates of signal as a feature vector by convolving a Gaussian window with the spike trains, the necessary inputs for the decoding algorithm, which is linear regression here, are obtained. Two patterns have been used for cross validation. The first pattern considers 60% of the data from the beginning of the signal as a train set and the remaining 40% of the signal as a test set and the second pattern is the opposite of the first one. Several methods have been used to evaluate the decoding algorithm used in the studies. In this paper, the correlation coefficient and coefficient of determination have been used. The correlation coefficient and coefficient of determination between the desired force and predicted force in linear resgression method, in average of three sessions for three rats, are equal to r=0.56 and =0.20 for the first pattern and r=0.55 and =0.30 for the second pattern respectively. These results show that firing rates of neurons are proper features to predict continous variables such as force. Besides, it can be concluded that linear regression is a suitable method for decoding a motor variable like force and follows the desired signal properly.
Bioelectrics
Marzieh Alirezaei Alavijeh; Ali Maleki
Volume 10, Issue 2 , August 2016, , Pages 187-196
Abstract
Brain-computer interface system based on Steady-state visual evoked potentials is taken into consideration due to advantages such as simplicity of installation and use of the system, enough accurate and acceptable Information transfer rate. In addition to these benefits, short processing time is also ...
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Brain-computer interface system based on Steady-state visual evoked potentials is taken into consideration due to advantages such as simplicity of installation and use of the system, enough accurate and acceptable Information transfer rate. In addition to these benefits, short processing time is also an important criterion to have a system that is applicable in real life and have the ability to use online. In this paper, a method based on standard CCA have been present for recognition of stimulus frequency. The proposed method is performed in two stages, offline and online. In the offline stage, the standard CCA is applied to the SSVEP and sin-cos reference signals. After that, template signals are constructed using weights that generate maximum correlation. In online stage, cross correlation between test signal and each template signals are calculated and the stimulus frequency is recognized. The greater accuracy of frequency recognition and less calculation time at the same time are shown by stimulation result.
