EEG source imaging assists decoding in a face recognition task

EEG based brain state decoding has numerous applications. State of the art decoding is based on processing of the multivariate sensor space signal, however evidence is mounting that EEG source reconstruction can assist decoding. EEG source imaging leads to high-dimensional representations and rather strong a priori information must be invoked. Recent work by Edelman et al. (2016) has demonstrated that introduction of a spatially focal source space representation can improve decoding of motor imagery. In this work we explore the generality of Edelman et al. hypothesis by considering decoding of face recognition. This task concerns the differentiation of brain responses to images of faces and scrambled faces and poses a rather difficult decoding problem at the single trial level. We implement the pipeline using spatially focused features and show that this approach is challenged and source imaging does not lead to an improved decoding. We design a distributed pipeline in which the classifier has access to brain wide features which in turn does lead to a 15% reduction in the error rate using source space features. Hence, our work presents supporting evidence for the hypothesis that source imaging improves decoding

Decoding Complex Imagery Hand Gestures

Brain computer interfaces (BCIs) offer individuals suffering from major disabilities an alternative method to interact with their environment. Sensorimotor rhythm (SMRs) based BCIs can successfully perform control tasks; however, the traditional SMR paradigms intuitively disconnect the control and real task, making them non-ideal for complex control scenarios. In this study, we design a new, intuitively connected motor imagery (MI) paradigm using hierarchical common spatial patterns (HCSP) and context information to effectively predict intended hand grasps from electroencephalogram (EEG) data. Experiments with 5 participants yielded an aggregate classification accuracy--intended grasp prediction probability--of 64.5\% for 8 different hand gestures, more than 5 times the chance level.Comment: This work has been submitted to EMBC 201

Comparing Features for Classification of MEG Responses to Motor Imagery

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Advancing Brain-Computer Interface System Performance in Hand Trajectory Estimation with NeuroKinect

Brain-computer interface (BCI) technology enables direct communication between the brain and external devices, allowing individuals to control their environment using brain signals. However, existing BCI approaches face three critical challenges that hinder their practicality and effectiveness: a) time-consuming preprocessing algorithms, b) inappropriate loss function utilization, and c) less intuitive hyperparameter settings. To address these limitations, we present \textit{NeuroKinect}, an innovative deep-learning model for accurate reconstruction of hand kinematics using electroencephalography (EEG) signals. \textit{NeuroKinect} model is trained on the Grasp and Lift (GAL) tasks data with minimal preprocessing pipelines, subsequently improving the computational efficiency. A notable improvement introduced by \textit{NeuroKinect} is the utilization of a novel loss function, denoted as $\mathcal{L}_{\text{Stat}}$. This loss function addresses the discrepancy between correlation and mean square error in hand kinematics prediction. Furthermore, our study emphasizes the scientific intuition behind parameter selection to enhance accuracy. We analyze the spatial and temporal dynamics of the motor movement task by employing event-related potential and brain source localization (BSL) results. This approach provides valuable insights into the optimal parameter selection, improving the overall performance and accuracy of the \textit{NeuroKinect} model. Our model demonstrates strong correlations between predicted and actual hand movements, with mean Pearson correlation coefficients of 0.92 ($\pm$0.015), 0.93 ($\pm$0.019), and 0.83 ($\pm$0.018) for the X, Y, and Z dimensions. The precision of \textit{NeuroKinect} is evidenced by low mean squared errors (MSE) of 0.016 ($\pm$0.001), 0.015 ($\pm$0.002), and 0.017 ($\pm$0.005) for the X, Y, and Z dimensions, respectively

Исследование электрической активности мозга, связанной с движениями: обзор

Робота присвячена розгляду проблем, що виникають при дослідженні діяльності мозку, пов'язаної з рухами. Зміни в корі головного мозку під час виконання руху, а також його уявлення, відображають нейронні мережі, сформовані для планування і реалізації конкретного руху. Наведено огляд методів первинної обробки зареєстрованої активності головного мозку, які можуть бути використані для підвищення значимості виділених ознак. Описано закономірності, які мають місце до початку руху і після нього. Представлені методи, які підходять для оцінки зв'язку як між активністю мозку і активністю м'язів, так і між активністю областей головного мозку. Крім того, розглянута можливість класифікації та прогнозування рухів разом з реконструкцією кінематичних властивостей.The work is devoted to consideration of different problems which arise in studying of the movement-related brain activity. Changes in the cortex activity during performing of the movement both real and imagery represent neural networks formed for planning and performing of the particular motion. The review of possible preprocessing methods of the registered brain activity for increasing significance of extracted features are shown. Regularities and patterns which take place before and after movement onset are described. The methods that suitable for connectivity estimations in case of cortico-muscular relationships and in case of evaluations between brain regions are shown. In addition, possibility of movement classification and prediction together with reconstruction of kinematics features of the motion are considered.Работа посвящена рассмотрению проблем, возникающих при изучении деятельности мозга, связанной с движениями. Изменения в коре головного мозга во время выполнения движения, а также его представления, отображают нейронные сети, сформированные для планирования и реализации конкретного движения. Приведен обзор методов первичной обработки зарегистрированной активности головного мозга, которые могут быть использованы для повышения значимости выделенных признаков. Описаны закономерности, которые имеют место до начала движения и после него. Представлены методы, подходящие для оценки связи как между активностью мозга и активностью мышц, так и между активностью областей головного мозга. Кроме того, рассмотрена возможность классификации и прогнозирования движений вместе с реконструкцией кинематических свойств

Spatial Detection of Multiple Movement Intentions from SAM-Filtered Single-Trial MEG for a high performance BCI

The objective of this study is to test whether human intentions to sustain or cease movements in right and left hands can be decoded reliably from spatially filtered single trial magneto-encephalographic (MEG) signals. This study was performed using motor execution and motor imagery movements to achieve a potential high performance Brain-Computer interface (BCI). Seven healthy volunteers, naïve to BCI technology, participated in this study. Signals were recorded from 275-channel MEG and synthetic aperture magnetometry (SAM) was employed as the spatial filter. The four-class classification for natural movement intentions was performed offline; Genetic Algorithm based Mahalanobis Linear Distance (GA-MLD) and direct-decision tree classifier (DTC) techniques were adopted for the classification through 10-fold cross-validation. Through SAM imaging, strong and distinct event related desynchronisation (ERD) associated with sustaining, and event related synchronisation (ERS) patterns associated with ceasing of hand movements were observed in the beta band (15 - 30 Hz). The right and left hand ERD/ERS patterns were observed on the contralateral hemispheres for motor execution and motor imagery sessions. Virtual channels were selected from these cortical areas of high activity to correspond with the motor tasks as per the paradigm of the study. Through a statistical comparison between SAM-filtered virtual channels from single trial MEG signals and basic MEG sensors, it was found that SAM-filtered virtual channels significantly increased the classification accuracy for motor execution (GA-MLD: 96.51 ± 2.43 %) as well as motor imagery sessions (GA-MLD: 89.69 ± 3.34%). Thus, multiple movement intentions can be reliably detected from SAM-based spatially-filtered single trial MEG signals. MEG signals associated with natural motor behavior may be utilized for a reliable high-performance brain-computer interface (BCI) and may reduce long-term training compared with conventional BCI methods using rhythm control. This may prove tremendously helpful for patients suffering from various movement disorders to improve their quality of life

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

Multiattention Adaptation Network for Motor Imagery Recognition

This work was supported in part by the National Natural Science Foundation of China under Grants Nos. 61873181 and 61922062Peer reviewedPostprin