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
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.
Neuro-Muscular Engineering
Amir Masoud Ahmadi; Sepideh Farakhor Seghinsara; Mohamad Reza Daliri; Vahid Shalchyan
Volume 11, Issue 1 , May 2017, , Pages 83-100
Abstract
The brain stimulation and its widespread use is one of the most important subjects in studies of neurophysiology. In brain electrical stimulation methods, following the surgery and electrode implantation, electrodes send electrical impulses to the specific targets in the brain. The use of this stimulation ...
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The brain stimulation and its widespread use is one of the most important subjects in studies of neurophysiology. In brain electrical stimulation methods, following the surgery and electrode implantation, electrodes send electrical impulses to the specific targets in the brain. The use of this stimulation method is provided therapeutic benefits for treatment chronic pain, essential tremor, Parkinson’s disease, major depression, and neurological movement disorder syndrome (dystonia). One area in which advancements have been recently made is in controlling the movement and navigation of animals in a specific pathway. It is important to identify brain targets in order to stimulate appropriate brain regions for all the applications listed above. An animal navigation system based on brain electrical stimulation is used to develop new behavioral models for the aim of creating a platform for interacting with the animal nervous system in the spatial learning task. In the context of animal navigation the electrical stimulation has been used either as creating virtual sensation for movement guidance or virtual reward for movement motivation. In this paper, different approaches and techniques of brain electrical stimulation for this application has been reviewed.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Alireza Mirjalili; Vahid Abootalebi; Mohammad Taghi Sadeghi
Volume 8, Issue 4 , February 2015, , Pages 305-323
Abstract
In recent years, Brain-Computer Interface (BCI) has been noted as a new means of communication between the human brain and his surroundings. In order to set up such a system, the collaboration of several blocks, such as data recording, signal processing and user interface are needed. The signal processing ...
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In recent years, Brain-Computer Interface (BCI) has been noted as a new means of communication between the human brain and his surroundings. In order to set up such a system, the collaboration of several blocks, such as data recording, signal processing and user interface are needed. The signal processing block, includes two units of preprocessing and pattern recognition. Pattern recognition block itself involves two phases: feature extraction and classification. In this paper, the sparse representation based classification (SRC) has been used in the classification block. There are two important issues in using the SRC. These are creating an appropriate dictionary matrix and adopting a proper method for finding the sparse solution for an input data. In this research study, the dictionary matrix is formed by extracting an optimal set of features from the training data. Toward this goal, the common spatial patterns algorithm (CSP) is first used. Sensitivity to noise and the over learning phenomena are the main drawbacks of the CSP algorithm. In order to remove these problems, the regularized common spatial patterns algorithm (RCSP) is employed. In previous studies in within the BCI framework, the standard BP algorithm has been used to find a sparse solution. The main disadvantage of the BP algorithm is that the method is computationally expensive. To overcome this weakness, a recently proposed algorithm namely the SL0 approach is used instead. Our experimental results show that when the number of training samples is limited, the RCSP algorithm outperforms the CSP one. Using the features derived from the RCSP, the average detection rate is in average increased by a factor of 7.53%. Our classification results also show that using the SL0 algorithm, the classification process is highly speeded up as compared to the BP algorithm while an almost equivalent accuracy is achieved.
