Brain electrical activity discriminant analysis using Reproducing Kernel Hilbert spaces

A deep an adequate understanding of the human brain functions has been an objective for interdisciplinar teams of scientists. Different types of technological acquisition methodologies, allow to capture some particular data that is related with brain activity. Commonly, the more used strategies are related with the brain electrical activity, where reflected neuronal interactions are reflected in the scalp and obtained via electrode arrays as time series. The processing of this type of brain electrical activity (BEA) data, poses some challenges that should be addressed carefully due their intrinsic properties. BEA in known to have a nonstationaty behavior and a high degree of variability dependenig of the stimulus or responses that are being adressed..

Brain electrical activity discriminant analysis using Reproducing Kernel Hilbert spaces

A deep an adequate understanding of the human brain functions has been an objective for interdisciplinar teams of scientists. Different types of technological acquisition methodologies, allow to capture some particular data that is related with brain activity. Commonly, the more used strategies are related with the brain electrical activity, where reflected neuronal interactions are reflected in the scalp and obtained via electrode arrays as time series. The processing of this type of brain electrical activity (BEA) data, poses some challenges that should be addressed carefully due their intrinsic properties. BEA in known to have a nonstationaty behavior and a high degree of variability dependenig of the stimulus or responses that are being adressed..

Discriminación de estados mentales mediante la extracción de patrones espaciales bajo restricciones de no estacionariedad e independencia de sujeto

graficas, tablasEvaluation of brain dynamics elicited by motor imagery (MI) tasks can contribute to clinical and learning applications. In this work, we propose four specific improvements for brain motor intention response analysis based on EEG recordings by considering the nonstationarity, nonlinearity of brain signals, inter- and intra-subject variability, aimed to provide physiological interpretability and the distintiveness between subjects neural response. Firstly, to build up the subject-level feature framework, a common representational space, is proposed that encodes the electrode (spatial) contribution, evolving through time and frequency domains. Three feature extraction methods were compared, providing insight into the possible limitations. Secondly, we present an Entropy-based method, termed \textit{VQEnt}, for estimation of ERD/S using quantized stochastic patterns as a symbolic space, aiming to improve their discriminability and physiological interpretability. The proposed method builds the probabilistic priors by assessing the Gaussian similarity between the input measured data and their reduced vector-quantized representation. The validating results of a bi-class imagine task database (left and right hand) prove that \textit{VQEnt} holds symbols that encode several neighboring samples, providing similar or even better accuracy than the other baseline sample-based algorithms of Entropy estimation. Besides, the performed ERD/S time-series are close enough to the trajectories extracted by the variational percentage of EEG signal power and fulfill the physiological MI paradigm. In BCI literate individuals, the \textit{VQEnt} estimator presents the most accurate outcomes at a lower amount of electrodes placed in the sensorimotor cortex so that reduced channel set directly involved with the MI paradigm is enough to discriminate between tasks, providing an accuracy similar to the performed by the whole electrode set. Thirdly, multi-subject analysis is to make inferences on the group/population level about the properties of MI brain activity. However, intrinsic neurophysiological variability of neural dynamics poses a challenge for devising efficient MI systems. Here, we develop a \textit{time-frequency} model for estimating the spatial relevance of common neural activity across subjects employing an introduced statistical thresholding rule. In deriving multi-subject spatial maps, we present a comparative analysis of three feature extraction methods: \textit{Common Spatial Patterns}, \textit{Functional Connectivity}, and \textit{Event-Related De/Synchronization}. In terms of interpretability, we evaluate the effectiveness in gathering MI data from collective populations by introducing two assumptions: \textit{i}) Non-linear assessment of the similarity between multi-subject data originating the subject-level dynamics; \textit{ii}) Assessment of time-varying brain network responses according to the ranking of individual accuracy performed in distinguishing distinct motor imagery tasks (left-hand versus right-hand). The obtained validation results indicate that the estimated collective dynamics differently reflect the flow of sensorimotor cortex activation, providing new insights into the evolution of MI responses. Lastly, we develop a data-driven estimator, termed {Deep Regression Network} (DRN), which jointly extracts and performs the regression analysis in order to assess the efficiency of the individual brain networks in practicing MI tasks. The proposed double-stage estimator initially learns a pool of deep patterns, extracted from the input data, in order to feed a neural regression model, allowing for infering the distinctiveness between subject assemblies having similar variability. The results, which were obtained on real-world MI data, prove that the DRN estimator fosters pre-training neural desynchronization and initial training synchronization to predict the bi-class accuracy response, thus providing a better understanding of the Brain--Computer Interface inefficiency of subjects. (Texto tomado de la fuente)La evaluación de la dinámica cerebral provocada por las tareas de imaginación motora (\textit{Motor Imagery - MI}) puede contribuir al desarrollo de aplicaciones clínicas y de aprendizaje. En este trabajo, se proponen cuatro mejoras específicas para el an\'lisis de la respuesta de la intención motora cerebral basada en registros de Electroencefalografía (EEG) al considerar la no estacionariedad, la no linealidad de las se\tilde{n}ales cerebrales y la variabilidad inter e intrasujeto, con el objetivo de proporcionar interpretabilidad fisiológica y la discriminación entre la respuesta neuronal de los sujetos. En primer lugar, para construir el marco de características a nivel de sujeto, se propone un espacio de representación común que codifica la contribución del electrodo (espacial) y como esta evoluciona a través de los dominios de tiempo y frecuencia. Tres métodos de extracción de características fueron comparados, proporcionando información sobre las posibles limitaciones. En segundo lugar, se presenta un método basado en Entropía, denominado \textit{VQEnt}, para la estimación de la desincronización relacionada a eventos (\textit{Event-Related De-Synchronization - ERD/S}) utilizando patrones estocásticos cuantificados en un espacio simbólico, con el objetivo de mejorar su discriminabilidad e interpretabilidad fisiol\'gica. El método propuesto construye los antecedentes probabilísticos mediante la evaluación de la similitud gaussiana entre los datos medidos de entrada y su representación cuantificada vectorial reducida. Los resultados de validación en una base de datos de tareas de imaginación bi-clase (mano izquierda y mano derecha) prueban que \textit{VQEnt} contiene símbolos que codifican varias muestras vecinas, proporcionando una precisión similar o incluso mejor que los otros algoritmos basados en estimación de entropía de referencia. Además, las series temporales de ERD/S calculadas son lo suficientemente cercanas a las trayectorias extraídas por el porcentaje de variación de la potencia de la señal EEG y cumplen con el paradigma fisiológico de MI. En individuos alfabetizados en BCI, el estimador \textit{VQEnt} presenta los resultados precisos con una menor cantidad de electrodos colocados en la corteza sensoriomotora, de modo que el conjunto reducido de canales directamente involucrados con el paradigma MI es suficiente para discriminar entre tareas. En tercer lugar, el análisis multisujeto consiste en hacer inferencias a nivel de grupo/población sobre las propiedades de la actividad cerebral de la imaginación motora. Sin embargo, la variabilidad neurofisiológica intrínseca de la dinámica neuronal plantea un desafío para el diseño de sistemas MI eficientes. En este sentido, se presenta un modelo de \textit{tiempo-frecuencia} para estimar la relevancia espacial de la actividad neuronal común entre sujetos empleando una regla de umbral estadística que deriva en mapas espaciales de múltiples sujetos. Se presenta un análisis comparativo de tres métodos de extracción de características: \textit{Patrones espaciales comunes}, \textit{Conectividad funcional} y \textit{De-sincronización relacionada con eventos}. En términos de interpretabilidad, evaluamos la efectividad en la recopilación de datos de MI para multisujetos mediante la introducción de dos suposiciones: \textit{i}) Evaluación no lineal de la similitud entre los datos de múltiples sujetos que originan la dinámica a nivel de sujeto; \textit{ii}) Evaluación de las respuestas de la red cerebral que varían en el tiempo de acuerdo con la clasificación de la precisión individual realizada al distinguir distintas tareas de imaginación motora (mano izquierda versus mano derecha). Los resultados de validación obtenidos indican que la dinámica colectiva estimada refleja de manera diferente el flujo de activación de la corteza sensoriomotora, lo que proporciona nuevos conocimientos sobre la evolución de las respuestas de MI. Por último, se muestra un estimador denominado {Red de regresión profunda} (\textit{Deep Regression Network - DRN}), que extrae y realiza conjuntamente un análisis de regresión para evaluar la eficiencia de las redes cerebrales individuales, de cada sujeto, en la práctica de tareas de MI. El estimador de doble etapa propuesto inicialmente aprende un conjunto de patrones profundos, extraídos de los datos de entrada, para alimentar un modelo de regresión neuronal, lo que permite inferir la distinción entre conjuntos de sujetos que tienen una variabilidad similar. Los resultados, que se obtuvieron con datos MI del mundo real, demuestran que el estimador DRN usa la desincronización neuronal previa al entrenamiento y la sincronización del entrenamiento inicial para predecir la respuesta de precisión bi-clase, proporcionando así una mejor comprensión de la ineficiencia de la respuesta de MI de los sujetos en las Interfaces Cerebro-Computador.DoctoradoDoctor en IngenieríaReconocimiento de PatronesEléctrica, Electrónica, Automatización Y Telecomunicaciones.Sede Manizale

Brain Music : Sistema generativo para la creación de música simbólica a partir de respuestas neuronales afectivas

gráficas, tablasEsta tesis de maestría presenta una metodología de aprendizaje profundo multimodal innovadora que fusiona un modelo de clasificación de emociones con un generador musical, con el propósito de crear música a partir de señales de electroencefalografía, profundizando así en la interconexión entre emociones y música. Los resultados alcanzan tres objetivos específicos: Primero, ya que el rendimiento de los sistemas interfaz cerebro-computadora varía considerablemente entre diferentes sujetos, se introduce un enfoque basado en la transferencia de conocimiento entre sujetos para mejorar el rendimiento de individuos con dificultades en sistemas de interfaz cerebro-computadora basados en el paradigma de imaginación motora. Este enfoque combina datos de EEG etiquetados con datos estructurados, como cuestionarios psicológicos, mediante un método de "Kernel Matching CKA". Utilizamos una red neuronal profunda (Deep&Wide) para la clasificación de la imaginación motora. Los resultados destacan su potencial para mejorar las habilidades motoras en interfaces cerebro-computadora. Segundo, proponemos una técnica innovadora llamada "Labeled Correlation Alignment"(LCA) para sonificar respuestas neurales a estímulos representados en datos no estructurados, como música afectiva. Esto genera características musicales basadas en la actividad cerebral inducida por las emociones. LCA aborda la variabilidad entre sujetos y dentro de sujetos mediante el análisis de correlación, lo que permite la creación de envolventes acústicos y la distinción entre diferente información sonora. Esto convierte a LCA en una herramienta prometedora para interpretar la actividad neuronal y su reacción a estímulos auditivos. Finalmente, en otro capítulo, desarrollamos una metodología de aprendizaje profundo de extremo a extremo para generar contenido musical MIDI (datos simbólicos) a partir de señales de actividad cerebral inducidas por música con etiquetas afectivas. Esta metodología abarca el preprocesamiento de datos, el entrenamiento de modelos de extracción de características y un proceso de emparejamiento de características mediante Deep Centered Kernel Alignment, lo que permite la generación de música a partir de señales EEG. En conjunto, estos logros representan avances significativos en la comprensión de la relación entre emociones y música, así como en la aplicación de la inteligencia artificial en la generación musical a partir de señales cerebrales. Ofrecen nuevas perspectivas y herramientas para la creación musical y la investigación en neurociencia emocional. Para llevar a cabo nuestros experimentos, utilizamos bases de datos públicas como GigaScience, Affective Music Listening y Deap Dataset (Texto tomado de la fuente)This master’s thesis presents an innovative multimodal deep learning methodology that combines an emotion classification model with a music generator, aimed at creating music from electroencephalography (EEG) signals, thus delving into the interplay between emotions and music. The results achieve three specific objectives: First, since the performance of brain-computer interface systems varies significantly among different subjects, an approach based on knowledge transfer among subjects is introduced to enhance the performance of individuals facing challenges in motor imagery-based brain-computer interface systems. This approach combines labeled EEG data with structured information, such as psychological questionnaires, through a "Kernel Matching CKA"method. We employ a deep neural network (Deep&Wide) for motor imagery classification. The results underscore its potential to enhance motor skills in brain-computer interfaces. Second, we propose an innovative technique called "Labeled Correlation Alignment"(LCA) to sonify neural responses to stimuli represented in unstructured data, such as affective music. This generates musical features based on emotion-induced brain activity. LCA addresses variability among subjects and within subjects through correlation analysis, enabling the creation of acoustic envelopes and the distinction of different sound information. This makes LCA a promising tool for interpreting neural activity and its response to auditory stimuli. Finally, in another chapter, we develop an end-to-end deep learning methodology for generating MIDI music content (symbolic data) from EEG signals induced by affectively labeled music. This methodology encompasses data preprocessing, feature extraction model training, and a feature matching process using Deep Centered Kernel Alignment, enabling music generation from EEG signals. Together, these achievements represent significant advances in understanding the relationship between emotions and music, as well as in the application of artificial intelligence in musical generation from brain signals. They offer new perspectives and tools for musical creation and research in emotional neuroscience. To conduct our experiments, we utilized public databases such as GigaScience, Affective Music Listening and Deap DatasetMaestríaMagíster en Ingeniería - Automatización IndustrialInvestigación en Aprendizaje Profundo y señales BiológicasEléctrica, Electrónica, Automatización Y Telecomunicaciones.Sede Manizale

Learning Interpretable Features of Graphs and Time Series Data

Graphs and time series are two of the most ubiquitous representations of data of modern time. Representation learning of real-world graphs and time-series data is a key component for the downstream supervised and unsupervised machine learning tasks such as classification, clustering, and visualization. Because of the inherent high dimensionality, representation learning, i.e., low dimensional vector-based embedding of graphs and time-series data is very challenging. Learning interpretable features incorporates transparency of the feature roles, and facilitates downstream analytics tasks in addition to maximizing the performance of the downstream machine learning models. In this thesis, we leveraged tensor (multidimensional array) decomposition for generating interpretable and low dimensional feature space of graphs and time-series data found from three domains: social networks, neuroscience, and heliophysics. We present the theoretical models and empirical results on node embedding of social networks, biomarker embedding on fMRI-based brain networks, and prediction and visualization of multivariate time-series-based flaring and non-flaring solar events

Spatio-spectral patterns based on stein kernel for EEG signal classification

El trastorno por déficit de atención con hiperactividad (TDAH) es un trastorno neurológico de inicio en la niñez que puede persistir en la adolescencia y la vida adulta, reduciendo la concentración, la memoria y la productividad. El principal inconveniente de las anomalías de la salud mental de este tipo es la técnica de diagnóstico tradicional, ya que se basa exclusivamente en una descripción sintomatológica sin considerar ningún dato biológico, lo que genera altas tasas de sobrediagnóstico. Para abordar el problema anterior, los investigadores clínicos están intentando extraer biomarcadores de TDAH a partir de señales electroencefalográficas (EEG) registradas. Entre los biomarcadores más comunes se encuentran la relación Theta / Beta y P300, de los cuales estudios recientes han demostrado una falta de importancia en las diferencias entre el TDAH y los sujetos de control. Además, otro gran desafío en el procesamiento del electroencefalograma viene dado por la sensibilidad de las señales, ya que pueden verse fácilmente afectadas por ruidos de fondo, artefactos musculares, movimientos de la cabeza y parpadeos que perjudican enormemente su calidad, lo que limita su introducción en aplicaciones del mundo real. Este trabajo propone una metodología de representación de señales de EEG para identificar discrepancias de respuestas inhibitorias en el sujeto, decodificar la estructura de datos y respaldar el diagnóstico de trastornos mentales. Para esto, primero desarrollamos un enfoque de extracción de características basado en los patrones espaciales comunes (CSP) de las señales de EEG para respaldar el diagnóstico de TDAH como se muestra en el capítulo 3. Luego, desarrollamos una metodología para la representación de señales de EEG que utiliza la similitud entre series de tiempo a través de sus matrices de covarianza en la variedad riemanniana de matrices semidefinitas positivas (PSD), utilizando la divergencia logdet de Jensen Bregman, el kernel de Stein y la alineación de kernel centrada (CKA) como una función de costo para realizar una optimización de filtros espaciales. Finalmente, en el capítulo 5 presentamos una metodología para el apoyo diagnóstico del TDAH. La propuesta implica el uso de los patrones espaciales óptimos desarrollados en el capítulo 4, una descomposición en los ritmos cerebrales y la decodificación discriminativa del capítulo 3. Las características subjetivas resultantes alimentaron un análisis discriminante lineal como herramienta de diagnóstico. La tasa de precisión alcanzada del 93% demuestra que el índice discriminativo basado en los patrones espaciales de stein supera a los biomarcadores convencionales en el diagnóstico de TDAH.Attention-Deficit/Hyperactivity Disorder (ADHD) is a childhood-onset neurological disorder that can persist in adolescence and adult life, reducing concentration, memory, and productivity. The main drawback with mental health abnormalities of this type is the traditional diagnostic technique. Since this is based exclusively on a symptomatological description without considering any biological data, leading to high overdiagnosis rates. To address the above problem, clinical researchers are attempting to extract ADHD biomarkers from recorded electroencephalographic (EEG) signals. Among the most common biomarkers are Theta/Beta Ratio and P300, of which recent studies have shown a lack of significance on the differences between ADHD and control subjects. Besides, another great challenge in EEG processing is given by the sensitivity of the signals, since they can be easily affected by background noise, muscle artifacts, head movements and flickering that greatly impair their quality, which limits its introduction into real world applications. This work proposes an EEG signal representation methodology for identifying subject-wise discrepancies of inhibitory responses, decoding the data structure, and supporting diagnosis of mental disorders. For this, first we develop a feature extraction approach based on the common spatial patterns (CSP) from EEG signals to support the ADHD diagnosis as show in chapter 3. Then, we develop a methodology for the representation of EEG signals that uses the similarity between time series through their covariance matrices in the Riemannian manifold of positive semidefinite matrices (PSD), using the logdet-divergence of Jensen Bregman, the Stein kernel, and Centered Kernel Alignment (CKA) as a cost function to perform a spatial filters optimization. Finally, in chapter 5 we present a methodology for the diagnostic support of ADHD. The proposal involves the use of the optimal spatial patterns developed in chapter 4, a decomposition in brain rhythms, and the discriminative decoding of chapter 3. The resulting subject-wise features fed a linear discriminant analysis as the supported-diagnosis tool. Achieved 93% accuracy rate proves that the discriminative index based on the stein spatial patterns outperforms conventional biomarkers in the ADHD diagnosis.MaestríaMagíster en Ingeniería EléctricaContents 1 List of Symbols and Abbreviations 6 1.1 Symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2 Abbrevations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2 Introduction 8 2.1 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 Justification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3 State of the art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.4 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4.1 General objective . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4.2 Specific objectives . . . . . . . . . . . . . . . . . . . . . . . 12 3 CSP-based discriminative capacity index from EEG 13 3.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1.1 Common Spatial Patterns . . . . . . . . . . . . . . . . . . . . 13 3.1.2 Discriminative decoding of CSP . . . . . . . . . . . . . . . . 14 3.2 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.2.1 Synthetic EEG records . . . . . . . . . . . . . . . . . . . . . 15 3.2.2 Real EEG records . . . . . . . . . . . . . . . . . . . . . . . . 16 3.2.3 Proposed scheme for feature extraction . . . . . . . . . . . . 19 3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.3.1 Discriminative decoding on simulated data . . . . . . . . . . 19 3.3.2 Feature extraction by discriminative decoding . . . . . . . . . 21 3.3.3 Diagnostic support of ADHD . . . . . . . . . . . . . . . . . 21 4 Multiple Kernel Stein Spatial Patterns 24 4.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.1.1 EEG Decomposition . . . . . . . . . . . . . . . . . . . . . . 24 4.1.2 Time-Series Similarity through the Stein Kernel for PSD Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.1.3 Spatial Filter Optimization Using Centered Kernel Alignment 27 4.1.4 Assembling of Multiple Kernel Representations . . . . . . . . 27 4.2 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.2.1 Dataset IIa from BCI Competition IV (BCICIV2a) . . . . . . 28 4.2.2 Proposed BCI Methodology . . . . . . . . . . . . . . . . . . 29 4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 4.3.1 Performance Results . . . . . . . . . . . . . . . . . . . . . . 30 4.3.2 Model Interpretability . . . . . . . . . . . . . . . . . . . . . 33 5 SSP-based discriminative capacity index from EEG supporting ADHD di agnosis 37 5.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 5.1.1 Brain rhythms EEG decomposition . . . . . . . . . . . . . . 38 5.1.2 Stein Spatial Patterns (SSP) . . . . . . . . . . . . . . . . . . 39 5.1.3 Discriminative decoding of SSP . . . . . . . . . . . . . . . . 39 5.1.4 Generative-supervised feature relevance . . . . . . . . . . . . 40 5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 6 Conclusions 45 6.1 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Spatio-spectral patterns based on Stein kernel for EEG signal classification

Attention-Deficit/Hyperactivity Disorder (ADHD) is a childhood-onset neurological disorder that can persist in adolescence and adult life, reducing concentration, memory, and productivity. The main drawback with mental health abnormalities of this type is the traditional diagnostic technique. Since this is based exclusively on a symptomatological description without considering any biological data, leading to high overdiagnosis rates. To address the above problem, clinical researchers are attempting to extract ADHD biomarkers from recorded electroencephalographic (EEG) signals. Among the most common biomarkers are Theta/Beta Ratio and P300, of which recent studies have shown a lack of significance on the differences between ADHD and control subjects. Besides, another great challenge in EEG processing is given by the sensitivity of the signals, since they can be easily affected by background noise, muscle artifacts, head movements and flickering that greatly impair their quality, which limits its introduction into real world applications. This work proposes an EEG signal representation methodology for identifying subject-wise discrepancies of inhibitory responses, decoding the data structure, and supporting diagnosis of mental disorders. For this, first we develop a feature extraction approach based on the common spatial patterns (CSP) from EEG signals to support the ADHD diagnosis as show in Chapter..

Enhancing Performance of Deep Learning Models for Epilepsy Seizure Detection

Epilepsy is a neurological condition marked by recurring seizures, leading to notable effects on the well-being of individuals experiencing it. Deep learning models have shown promising results in detecting and classifying epilepsy based on electroencephalogram (EEG) data and Magnetic Resonance imaging (MRI). However, achieving high performance in epilepsy detection requires continuous efforts to enhance the accuracy and reliability of these models. This study introduces multiple approaches for improving the effectiveness of deep learning models designed for detecting epilepsy. Initially, we employ data preprocessing methods to cleanse and prepare the input data, including noise removal, data normalization, and handling missing values. Additionally, data augmentation methods, such as random rotations, translations, and scaling are employed to increase the diversity and generalizability of the training data. Secondly, various model architectures are explored to improve the model's ability to detect epilepsy. CNNs and RNNs are commonly employed, and their configurations are experimented with by adjusting the depth, and width, and adding additional layers such as residual connections or attention mechanisms. Furthermore, hyperparameter tuning techniques are employed to enhance the deep learning model's efficiency. Thoughtful choices are made regarding hyperparameters like learning rate, batch size, and regularization methods and are carefully selected through approaches like grid search or random exploration conducted to discover the best possible setup that maximizes the model's effectiveness. By implementing these strategies, the performance of deep learning models for epilepsy detection has been significantly enhanced. The improved accuracy and reliability of these models offer great potential for early detection and intervention, leading to better management and treatment outcomes for individuals living with epilepsy

Temporal-frequency-phase feature classification using 3D-convolutional neural networks for motor imagery and movement

Recently, convolutional neural networks (CNNs) have been widely applied in brain-computer interface (BCI) based on electroencephalogram (EEG) signals. Due to the subject-specific nature of EEG signal patterns and the multi-dimensionality of EEG features, it is necessary to employ appropriate feature representation methods to enhance the decoding accuracy of EEG. In this study, we proposed a method for representing EEG temporal, frequency, and phase features, aiming to preserve the multi-domain information of EEG signals. Specifically, we generated EEG temporal segments using a sliding window strategy. Then, temporal, frequency, and phase features were extracted from different temporal segments and stacked into 3D feature maps, namely temporal-frequency-phase features (TFPF). Furthermore, we designed a compact 3D-CNN model to extract these multi-domain features efficiently. Considering the inter-individual variability in EEG data, we conducted individual testing for each subject. The proposed model achieved an average accuracy of 89.86, 78.85, and 63.55% for 2-class, 3-class, and 4-class motor imagery (MI) classification tasks, respectively, on the PhysioNet dataset. On the GigaDB dataset, the average accuracy for 2-class MI classification was 91.91%. For the comparison between MI and real movement (ME) tasks, the average accuracy for the 2-class were 87.66 and 80.13% on the PhysioNet and GigaDB datasets, respectively. Overall, the method presented in this paper have obtained good results in MI/ME tasks and have a good application prospect in the development of BCI systems based on MI/ME