Survey analysis for optimization algorithms applied to electroencephalogram

This paper presents a survey for optimization approaches that analyze and classify Electroencephalogram (EEG) signals. The automatic analysis of EEG presents a significant challenge due to the high-dimensional data volume. Optimization algorithms seek to achieve better accuracy by selecting practical features and reducing unwanted features. Forty-seven reputable research papers are provided in this work, emphasizing the developed and executed techniques divided into seven groups based on the applied optimization algorithm particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), grey wolf optimizer (GWO), Bat, Firefly, and other optimizer approaches). The main measures to analyze this paper are accuracy, precision, recall, and F1-score assessment. Several datasets have been utilized in the included papers like EEG Bonn University, CHB-MIT, electrocardiography (ECG) dataset, and other datasets. The results have proven that the PSO and GWO algorithms have achieved the highest accuracy rate of around 99% compared with other techniques

Odhad emocí a duševní koncentrace pomocí technik Deep Learningu

The purpose of this work is to evaluate the brain waves of humans with deep learn- ing methods and evolutionary computation techniques, and to verify the performance of applied techniques. In this thesis, we apply well–known metaheuristics and Artificial Neural Networks for classifying human mental activities using electroencephalographic signals. We developed a Brain–Computer Interface system that is able to process elec- troencephalographic signals and classify mental concentration versus relaxation. The system is able to automatically extract and learn representation of the given data. Based on scientific protocols we designed the Brain–Computer Interface experiments and we created an original and relevant data for the industrial and academic community. Our experimental data is available to the scientific community. In the experiments we used an electroencephalographic based device for collecting brain information form the subjects during specific activities. The collected data represents brain waves of subjects who was stimulated by writing tasks. Furthermore, we selected the best combination of the input features (brain waves information) using the following two metaheuristic techniques: Simulated Annealing and Geometric Particle Swarm Optimization. We applied a specific type of Artificial Neural Network, named Echo State Network, for solving the mapping between brain information and subject activities. The results indicate that it is possible to estimate the human con- centration using few electroencephalographic signals. In addition, the proposed system is developed with a fast and robust learning technique that can be easily adapted accord- ing to each subject. Moreover, this approach does not require powerful computational resources. As a consequence, the proposed system can be used in environments which are computationally limited and/or where the computational time is an important issue.Cílem práce je ohodnocení lidských mozkových vln s využitím metod hlubokého učení (deep learning) a evolučních výpočetních technik a pro ověření výkonu aplikovaných technik. V diplomové práci jsou využity dobře známé metaheuristiky a umělé neuronové sítě pro klasifikaci lidských mentálních aktivit za použití elektroencefalografických signálů. Bylo vyvinuto rozhraní mozek-počítač, které je schopno zpracovat elektroencefalografické signály a klasifikovat mentální soustředění v porovnání s relaxací. Systém je schopen automaticky extrahovat a naučit se reprezentaci daných dat. Na základě vědeckých protokolů byl navržen experiment pro rozhraní mozek-počítač a byla vytvořena původní a relevantní data pro průmyslovou a akademickou komunitu. Vygenerovaná pokusná data jsou přístupné pro vědeckou komunitu. V rámci experimentů bylo využito zařízení založené na encefalografii pro sběr mozkových signálů subjektu během specifických aktivit. Nasbíraná data reprezentují mozkové vlny subjektu, který byl stimulován psaním úloh. Dále byla vybrána nejlepší kombinace vstupních vlastností (informace o mozkové vlně) s využitím následujících dvou metaheuristických metod: simulovaného žíhání a geometrické optimalizace hejnem částic. Umělá neuronová síť, která se nazývá Echo State síť, byla aplikována pro řešení mapování mezi informacemi z mozku a aktivitami subjektu. Výsledky ukazují, že je možné odhadnout lidskou aktivitu pomocí několika encefalografických signálů. Kromě toho, navrhovaný systém je vyvinut s využitím rychlých a robustních učících technik, které mohou být jednoduše přizpůsobeny podle jednotlivých subjektů. Tento přístup navíc nevyžaduje výkonné výpočetní prostředky. V důsledku toho může být systém využit v prostředí, které jsou výpočetně omezeny a/nebo v případech, kdy výpočetní čas je důležitým hlediskem.460 - Katedra informatikyvýborn

EEG Based Emotion Prediction with Neural Network Models

The term "emotion" refers to an individual\u27s response to an event, person, or condition. In recent years, there has been an increase in the number of papers that have studied emotion estimation. In this study, a dataset based on three different emotions, utilized to classify feelings using EEG brainwaves, has been analysed. In the dataset, six film clips have been used to elicit positive and negative emotions from a male and a female. However, there has not been a trigger to elicit a neutral mood. Various classification approaches have been used to classify the dataset, including MLP, SVM, PNN, KNN, and decision tree methods. The Bagged Tree technique which is utilized for the first time has been achieved a 98.60 percent success rate in this study, according to the researchers. In addition, the dataset has been classified using the PNN approach, and achieved a success rate of 94.32 percent

Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported

Driver drowsiness detection using Gray Wolf Optimizer based on voice recognition

Globally, drowsiness detection prevents accidents. Blood biochemicals, brain impulses, etc., can measure tiredness. However, due to user discomfort, these approaches are challenging to implement. This article describes a voice-based drowsiness detection system and shows how to detect driver fatigue before it hampers driving. A neural network and Gray Wolf Optimizer are used to classify sleepiness automatically. The recommended approach is evaluated in alert and sleep-deprived states on the driver tiredness detection voice real dataset. The approach used in speech recognition is mel-frequency cepstral coefficients (MFCCs) and linear prediction coefficients (LPCs). The SVM algorithm has the lowest accuracy (71.8%) compared to the typical neural network. GWOANN employs 13-9-7-5 and 30-20-13-7 neurons in hidden layers, where the GWOANN technique had 86.96% and 90.05% accuracy, respectively, whereas the ANN model achieved 82.50% and 85.27% accuracy, respective

Emotion Classification through Nonlinear EEG Analysis Using Machine Learning Methods

Background: Emotion recognition, as a subset of affective computing, has received considerable attention in recent years. Emotions are key to human-computer interactions. Electroencephalogram (EEG) is considered a valuable physiological source of information for classifying emotions. However, it has complex and chaotic behavior.Methods: In this study, an attempt is made to extract important nonlinear features from EEGs with the aim of emotion recognition. We also take advantage of machine learning methods such as evolutionary feature selection methods and committee machines to enhance the classification performance. Classification performed concerning both arousal and valence factors.Results: Results suggest that the proposed method is successful and comparable to the previous works. A recognition rate equal to 90% achieved, and the most significant features reported. We apply the final classification scheme to 2 different databases including our recorded EEGs and a benchmark dataset to evaluate the suggested approach.Conclusion: Our findings approve of the effectiveness of using nonlinear features and a combination of classifiers. Results are also discussed from different points of view to understand brain dynamics better while emotion changes. This study reveals useful insights about emotion classification and brain-behavior related to emotion elicitation

Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Voice Recognition

Globally, drowsiness detection prevents accidents. Blood biochemicals, brain impulses, etc., can measure tiredness. However, due to user discomfort, these approaches are challenging to implement. This article describes a voice-based drowsiness detection system and shows how to detect driver fatigue before it hampers driving. A neural network and Gray Wolf Optimizer are used to classify sleepiness automatically. The recommended approach is evaluated in alert and sleep-deprived states on the driver tiredness detection voice real dataset. The approach used in speech recognition is mel-frequency cepstral coefficients (MFCCs) and linear prediction coefficients (LPCs). The SVM algorithm has the lowest accuracy (71.8%) compared to the typical neural network. GWOANN employs 13-9-7-5 and 30-20-13-7 neurons in hidden layers, where the GWOANN technique had 86.96% and 90.05% accuracy, respectively, whereas the ANN model achieved 82.50% and 85.27% accuracy, respectively

Domain knowledge-assisted multi-objective evolutionary algorithm for channel selection in brain-computer interface systems

BackgroundFor non-invasive brain-computer interface systems (BCIs) with multiple electroencephalogram (EEG) channels, the key factor limiting their convenient application in the real world is how to perform reasonable channel selection while ensuring task accuracy, which can be modeled as a multi-objective optimization problem. Therefore, this paper proposed a two-objective problem model for the channel selection problem and introduced a domain knowledge-assisted multi-objective optimization algorithm (DK-MOEA) to solve the aforementioned problem.MethodsThe multi-objective optimization problem model was designed based on the channel connectivity matrix and comprises two objectives: one is the task accuracy and the other one can sensitively indicate the removal status of channels in BCIs. The proposed DK-MOEA adopted a two-space framework, consisting of the population space and the knowledge space. Furthermore, a knowledge-assisted update operator was introduced to enhance the search efficiency of the population space by leveraging the domain knowledge stored in the knowledge space.ResultsThe proposed two-objective problem model and DK-MOEA were tested on a fatigue detection task and four state-of-the-art multi-objective evolutionary algorithms were used for comparison. The experimental results indicated that the proposed algorithm achieved the best results among all the comparative algorithms for most cases by the Wilcoxon rank sum test at a significance level of 0.05. DK-MOEA was also compared with a version without the utilization of domain knowledge and the experimental results validated the effectiveness of the knowledge-assisted mutation operator. Moreover, the comparison between DK-MOEA and a traditional classification algorithm using all channels demonstrated that DK-MOEA can strike the balance between task accuracy and the number of selected channels.ConclusionThe formulated two-objective optimization model enabled the selection of a minimal number of channels without compromising classification accuracy. The utilization of domain knowledge improved the performance of DK-MOEA. By adopting the proposed two-objective problem model and DK-MOEA, a balance can be achieved between the number of the selected channels and the accuracy of the fatigue detection task. The methods proposed in this paper can reduce the complexity of subsequent data processing and enhance the convenience of practical applications