The effects of EEG feature extraction using multi-wavelet decomposition for mental tasks classification
15 Apr 2019-pp 139-146
TL;DR: A new identification method based on electroencephalogram (EEG) signals is proposed which achieved the highest accuracy when using a visual counting mental task.
Abstract: In modern life, the identification system is considered as one of the most challenging projects because identity authentication needs to be secure. Researchers have developed digital authentication techniques which are implemented in society. One of these techniques is using biometric technology which is commonly known as face recognition, voice recognition, and fingerprinting. These techniques have achieved a high level of authentication but are subject to hacking or counterfeiting. In this paper, a new identification method based on electroencephalogram (EEG) signals is proposed. The EEG method uses a standard EEG database which deals with five different thought patterns or mental tasks which are multiplication, baseline, letter composition, rotation, and visual board counting. Using ANN (artificial neural network) classifier, EEG signals were classified. The performance of this proposed method is evaluated using five criteria: (accuracy, sensitivity, specificity, F-Score measure, and false acceptance rate). The experimental results show that the EEG features extraction with wavelet 10 decomposition levels can achieve better than 5 decomposition levels for all mental tasks. The proposed method achieved the highest accuracy when using a visual counting mental task.
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TL;DR: A systematic review of the published articles in the last five years aims to help in choosing the appropriate deep neural network architecture and other hyperparameters for developing MI EEG-based BCI systems.
151 citations
TL;DR: A new hybrid of MVO algorithm with the K-means clustering algorithm is proposed, i.e., the H-MVO algorithm, which aims at improving the global (diversification) ability of the search and finding a better cluster partition.
Abstract: Text clustering has been widely utilized with the aim of partitioning specific document collection into different subsets using homogeneity/heterogeneity criteria. It has also become a very complicated area of research, including pattern recognition, information retrieval, and text mining. Metaheuristics are typically used as efficient approaches for the text clustering problem. The multi-verse optimizer algorithm (MVO) involves a stochastic population-based algorithm. It has been recently proposed and successfully utilized to tackle many hard optimization problems. However, a recently applied research trend involves hybridizing two or more algorithms with the aim of obtaining a superior solution regarding the problems of optimization. In this paper, a new hybrid of MVO algorithm with the K-means clustering algorithm is proposed, i.e., the H-MVO algorithm with the aims of enhancing the quality of initial candidate solutions, as well as the best solution, which is produced by MVO at each iteration. This hybrid algorithm aims at improving the global (diversification) ability of the search and finding a better cluster partition. The proposed H-MVO effectiveness was tested on five standard datasets, which are used in the domain of data clustering, as well as six standard text datasets, which are utilized in the domain of text document clustering, in addition to two scientific articles’ datasets. The experiments showed that K-means hybridized MVO improves the results in terms of high convergence rate, accuracy, error rate, purity, entropy, recall, precision, and F-measure criteria. In general, H-MVO has outperformed or at least proven to be highly competitive compared to the original MVO algorithm and with well-known optimization algorithms like KHA, HS, PSO, GA, H-PSO, and H-GA and the clustering techniques like K-mean, K-mean++, DBSCAN, agglomerative, and spectral clustering techniques.
33 citations
Additional excerpts
...These algorithms are categorized into population-based and single-based algorithms based on the solutions number that are provided in each iteration [14] such as ray optimization algorithm (ROA) [15], harmony search (HS) [16], grey wolf optimizer (GWO) [17], cuckoo search (CS) [18], salp swarm algorithm (SSA) [19], fruit fly optimization algorithm (FFOA) [20], dragonfly algorithm (DA) [21], krill herd algorithm (KHA) [22], teachinglearning-based optimization (TLBO) [23], dolphin echolocation (DE), ant lion optimizer (ALO) [24, 25], particle swarm optimization (PSO) [26], and ant colony optimization (ACO) [27]....
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TL;DR: A new formulation for smart home battery (SHB) is proposed for PSPSH that reduces the effect of restrictions in obtaining the optimal/near-optimal solutions and exhibits and yields better performance than the other compared algorithms in almost all scenarios.
Abstract: The power scheduling problem in a smart home (PSPSH) refers to the timely scheduling operations of smart home appliances under a set of restrictions and a dynamic pricing scheme(s) produced by a power supplier company (PSC). The primary objectives of PSPSH are: (I) minimizing the cost of the power consumed by home appliances, which refers to electricity bills, (II) balance the power consumed during a time horizon, particularly at peak periods, which is known as the peak-to-average ratio, and (III) maximizing the satisfaction level of users. Several approaches have been proposed to address PSPSH optimally, including optimization and non-optimization based approaches. However, the set of restrictions inhibit the approach used to obtain the optimal solutions. In this paper, a new formulation for smart home battery (SHB) is proposed for PSPSH that reduces the effect of restrictions in obtaining the optimal/near-optimal solutions. SHB can enhance the scheduling of smart home appliances by storing power at unsuitable periods and use the stored power at suitable periods for PSPSH objectives. PSPSH is formulated as a multi-objective optimization problem to achieve all objectives simultaneously. A robust swarm-based optimization algorithm inspired by the grey wolf lifestyle called grey wolf optimizer (GWO) is adapted to address PSPSH. GWO has powerful operations managed by its dynamic parameters that maintain exploration and exploitation behavior in search space. Seven scenarios of power consumption and dynamic pricing schemes are considered in the simulation results to evaluate the proposed multi-objective PSPSH using SHB (BMO-PSPSH) approach. The proposed BMO-PSPSH approach’s performance is compared with that of other 17 state-of-the-art algorithms using their recommended datasets and four algorithms using the proposed datasets. The proposed BMO-PSPSH approach exhibits and yields better performance than the other compared algorithms in almost all scenarios.
30 citations
TL;DR: The experimental results showed the feasibility of the proposed tractor driving control method based on EEG signal combined with RNN-TL deep learning algorithm which can work with the displacement error less than 6.7 mm when the tractor speed is less than 50 km/h.
Abstract: Nowadays, fieldwork is often accompanied by tight schedules, which tends to strain the shoulder muscles due to high-intensity work. Moreover, it is difficult and stressful for the disabled to drive agricultural machinery. Besides, current artificial intelligence technology could not fully realize tractor autonomous driving because of a high uncertain filed environment and short interruptions of satellite navigation signal shaded by trees. To reduce manual operations, a tractor assistant driving control method was proposed based on the human-machine interface utilizing the electroencephalographic (EEG) signal. First, the EEG signals of the tractor drivers were collected by a low-cost brain-computer interface (BCI), followed by denoising using a wavelet packet. Then the spectral features of EEG signals were calculated and extracted as the input of Recurrent Neural Network (RNN). Finally, the EEG-aided RNN driving model was trained for tractor driving robot control such as straight going, brake, left turn, and right turn operations, which control accuracy was 94.5% and time cost was 0.61 ms. Also, 8 electrodes were selected by the PCA algorithm for the design of a portable EEG controller. And the control accuracy reached 93.1% with the time cost of 0.48 ms. To solve the incomplete driving data set in the actual world because some driving manners may cause dangerous or even death, RNN-TL algorithm was employed by creating the complete driving data in the virtual environment followed by transferring the driving control experience to the actual world with small actual driving data set in the field, which control accuracy was 93.5% and time consumption was 0.48 ms. The experimental results showed the feasibility of the proposed tractor driving control method based on EEG signal combined with RNN-TL deep learning algorithm which can work with the displacement error less than 6.7 mm when the tractor speed is less than 50 km/h.
19 citations
TL;DR: A new ensemble method for an automatic topic extraction (TE) has been proposed in this paper, from a set of scientific publications in the form of text documents with the purpose of extracting topics from clustered documents, and revealed that the suggested ensembled TE method outperformed the entire comparative methods.
Abstract: For text document clustering (TDC), a novel hybrid of the multi-verse optimizer (MVO) algorithm and k-means (also called H-MVO) are proposed in this work. Moreover, a new ensemble method for an automatic topic extraction (TE) has been proposed in this paper, from a set of scientific publications in the form of text documents with the purpose of extracting topics from clustered documents. Often, the existing TE methods draw upon the statistical theory. However, the results might be different when the same clustered document is utilized. Consequently, there can be imprecise results, which are related to the extracted topics from the clustered documents owing to the behavior of the TE methods. As a result, the vigorous characteristics of the TE methods are ensembled, thereby empowering the accuracy of the extracted topics. The results, which were yielded by H-MVO for TDC, were compared against 14 well-regarded methods, involving five clustering methods, in addition to seven metaheuristic algorithms, as well as two hybrid optimization algorithms. Also, the results, which were generated by the introduced ensembled TE method, were compared against those, which were produced by five established statistical methods in the literature. As a result, the findings revealed that the suggested ensembled TE method outperformed the entire comparative methods, thereby utilizing all the external measurements for almost the entire datasets. Moreover, the new method can complement the advantages of the five previously proposed methods. Accordingly, more advanced results were obtained.
13 citations
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TL;DR: This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BCI2000 system is based upon and gives examples of successful BCI implementations using this system.
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TL;DR: This paper proposes a new algorithm, namely, flower pollination algorithm, inspired by the pollination process of flowers, and uses it to solve a nonlinear design benchmark, which shows the convergence rate is almost exponential.
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1,525 citations
TL;DR: In this article, a new algorithm, namely, flower pollination algorithm, inspired by the pollination process of flowers, was proposed, which is more efficient than both GA and PSO.
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TL;DR: The feasibility of establishing an alternative mode of communication between man and his surroundings using only the subject's brain waves was studied, indicating that it is possible to accurately distinguish between any two of the five tasks investigated.
Abstract: The feasibility of establishing an alternative mode of communication between man and his surroundings was studied. The form of communication proposed uses only the subject's brain waves, with no overt physical action required. The subject's electroencephalograms (EEG) were recorded while various mental tasks designed to elicit hemispheric responses were performed. Features formed from the EEG recording were then used as inputs into a Bayes quadratic classifier to test classification accuracy between the various tasks. The results obtained indicate that it is possible to accurately distinguish between any two of the five tasks investigated. A comparison between three different methods for creating the feature sets is also presented. >
466 citations