Brain Computer Interface / BCI / Neural Control Int. / NCI / Mind Machine Int. / MMI / Direct Neural Int. / DNI / Brain Machine Int. / BMI
Fatemeh Ghomi; Amin Mahnam; Mohammad Reza Yazdchi
Volume 12, Issue 2 , September 2018, , Pages 97-109
Abstract
Over the past few decades, the brain-computer interfaces (BCI) based on motor imagery has been widely developed to help people with motor disability. The advantage of this type of BCI as an endogenous system is, no need for external stimulation, and natural control. One of the major challenges to make ...
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Over the past few decades, the brain-computer interfaces (BCI) based on motor imagery has been widely developed to help people with motor disability. The advantage of this type of BCI as an endogenous system is, no need for external stimulation, and natural control. One of the major challenges to make these systems practical is to reduce the number of recording electrodes. In this study, only two EEG channels (C3 and C4) were used for detecting the imagery of left and right-hand movements. The features used were band powers (BP), some time domain parameters (TDP) and an adaptive autoregressive model (AAR). For classification, linear discriminant analysis (LDA), a well-known and simple classifier was used.The data was taken from the third BCI Competition. Our results confirm that BP features provide the most robust and effective features for accurate recognition. It was shown that combining the BP with TDP and AAR features can improve the accuracy of classification. However, implementing BP and TDP features is proposed for online classification where short computational cost is important. A maximum steepness of the mutual information (STMI) of 0.2582 was achieved in this study that could win the second place in the BCI Competition III. Left and right motor imagery (MI) tasks can be discriminated with an average classification accuracy of 85% and Kappa of 70%.
Human Computer Interaction / HCI
Mohsen Keshtkar; Amin Mahnam; Pegah Poladian
Volume 10, Issue 4 , January 2017, , Pages 279-290
Abstract
Steady State Visual Evoked Potentials (SSVEP) have been widely used in development of Brain Computer Interfaces (BCI). However, it is still a research challenge to have visual stimuli which provide strong SSVEP response while produce little eye fatigue. In this study, rectangular, sinosoidal, sawtooth ...
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Steady State Visual Evoked Potentials (SSVEP) have been widely used in development of Brain Computer Interfaces (BCI). However, it is still a research challenge to have visual stimuli which provide strong SSVEP response while produce little eye fatigue. In this study, rectangular, sinosoidal, sawtooth waveforms applied to a LED were compared with sum of two sinusoidals and a frequency modulated waveform to determine the most appropriate visual stimulus for realization of a BCI system. Moreover, circular, ring and anti-phase two rectangular flickers were generated by Cogent toolbox on a laptop screen and compared. Experiments were performed on 12 participants to determine the SSVEP response and eye fatigue corresponding to each of these visual stimuli. Experiments with the waveforms demonstrate that sum of two sine waves generated significantly lower SSVEP amplitude, but the responses for other four waveforms were not significantly different. On the other hand, the frequency modulated waveform resulted in the least eye fatigue significantly lower from other waveforms. Therefore, considering both criteria, frequency modulated waveform can provide superor performance in a BCI system with an average response of 17.3 pV2 and 1.58 fatigue level in a 1-4 fatigue scale. Experiments with visual stimuli on LCD showed that circular stimuli provided highest and anti-phase rectangular the lowest response. But all of them produced high levels of eye fatigue. Although, Circular stimuli had the highest power (26.7pV2) but due to its related high eye fatigue (3.8) it is not recommended for practical applications. In conclusion it is recommended to use frequency modulated visual stimuli for development of practical BCI systems to satisfy both strong response and low eye fatigue criteria.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mohammad Ali Manouchehri; Vahid Abootalebi; Amin Mahnam
Volume 9, Issue 2 , July 2015, , Pages 205-214
Abstract
SSVEP-based BCI systems have attracted attention of many researchers due to their high signal to noise ratio, high information transfer rate and being easy for use. The processing goal of these systems is to detect the stimulus frequency of EEG signal. Among the processing methods for frequency identification ...
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SSVEP-based BCI systems have attracted attention of many researchers due to their high signal to noise ratio, high information transfer rate and being easy for use. The processing goal of these systems is to detect the stimulus frequency of EEG signal. Among the processing methods for frequency identification in SSVEP-based BCI systems, LASSO algorithm has gained great acceptance. Although LASSO has acceptable performance in SSVEP-based BCI systems, it doesn't consider the phase of recorded EEG signal for creating the reference signal. In this paper, the idea of correcting the phase of the reference signal with respect to recorded EEG signal was investigated and a new method called phase corrected LASSO was proposed. For this purpose, first, the optimal EEG channel for frequency identification was determined and then, the performance of the phase corrected LASSO method was compared with standard LASSO method. The results show that the phase corrected LASSO method has better performance compared with the standard LASSO method.
Neuro-Muscular Engineering
Amin Mahnam; Seyed Mohammad Firouzabadi; Seyed Mohammad Reza Hashemi Golpayegani
Volume -1, Issue 1 , June 2004, , Pages 65-76
Abstract
In recent years, various methods have been suggested to improve selectivity in electrical stimulation of neural fibers or cells. One of these methods is the use of depolarizing under-threshold prepulse to selectively stimulate fibers far from the electrode, without excitation of nearer fibers. In this ...
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In recent years, various methods have been suggested to improve selectivity in electrical stimulation of neural fibers or cells. One of these methods is the use of depolarizing under-threshold prepulse to selectively stimulate fibers far from the electrode, without excitation of nearer fibers. In this paper, by implementing a nonlinear model of neural fiber and simulating electrical stimulation of the model, the effect of changes in various parameters of rectangular and stepwise prepulses on the range of applicability of this technique in selective stimulation of fibers in different distances from the electrode and with different diameters has been studied. This study has led to suggest a new waveform for the prepulse; ramp prepulse. The applicability of this prepulse has been studied also. The superiority of this prepulse in comparison with previous suggested ones has been shown. Using this prepulse, it is possible to stimulate selectively fibers in broader range of distances and diameters. Therefore in stimulating neural fibers in spinal cord or peripheral fibers or even neural fibers of special senses, the use of this prepulse can improve distinguishability of fibers in their stimulation.
