Deep Learning Methods for EEG Signals Classification of Motor Imagery in BCI

EEG signals are obtained from an EEG device after recording the user's brain signals. EEG signals can be generated by the user after performing motor movements or imagery tasks. Motor Imagery (MI) is the task of imagining motor movements that resemble the original motor movements. Brain Computer Interface (BCI) bridges interactions between users and applications in performing tasks. Brain Computer Interface (BCI) Competition IV 2a was used in this study. A fully automated correction method of EOG artifacts in EEG recordings was applied in order to remove artifacts and Common Spatial Pattern (CSP) to get features that can distinguish motor imagery tasks. In this study, a comparative studies between two deep learning methods was explored, namely Deep Belief Network (DBN) and Long Short Term Memory (LSTM). Usability of both deep learning methods was evaluated using the BCI Competition IV-2a dataset. The experimental results of these two deep learning methods show average accuracy of 50.35% for DBN and 49.65% for LSTM

Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots. More specifically, motor imagery EEG (MI-EEG), which reflects a subjects active intent, is attracting increasing attention for a variety of BCI applications. Accurate classification of MI-EEG signals while essential for effective operation of BCI systems, is challenging due to the significant noise inherent in the signals and the lack of informative correlation between the signals and brain activities. In this paper, we propose a novel deep neural network based learning framework that affords perceptive insights into the relationship between the MI-EEG data and brain activities. We design a joint convolutional recurrent neural network that simultaneously learns robust high-level feature presentations through low-dimensional dense embeddings from raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various artifacts such as background activities. The proposed approach has been evaluated extensively on a large- scale public MI-EEG dataset and a limited but easy-to-deploy dataset collected in our lab. The results show that our approach outperforms a series of baselines and the competitive state-of-the- art methods, yielding a classification accuracy of 95.53%. The applicability of our proposed approach is further demonstrated with a practical BCI system for typing.Comment: 10 page

Procesado on the edge de señales EEG para reconocimiento de tareas de imaginación motora

The development of brain-computer interfaces aims to enable communication between humans and machines, mainly through devices capable of acquiring and interpreting the electrical signals emitted by the user's brain when carrying out specific and intentional actions. This work presents the implementation on an FPGA of a compact convolutional neural network capable of correctly classifying this type of signals.El desarrollo de las interfaces cerebro-computador busca habilitar la comunicación entre humanos y máquinas, principalmente mediante dispositivos capaces de adquirir e interpretar las señales eléctricas emitidas por el cerebro del usuario al realizar acciones concretas e intencionadas. Este trabajo presenta la implementación sobre una FPGA de una red neuronal convolucional compacta capaz de clasificar correctamente este tipo de señales

Procesado on the edge de señales EEG para reconocimiento de tareas de imaginación motora

The development of brain-computer interfaces aims to enable communication between humans and machines, mainly through devices capable of acquiring and interpreting the electrical signals emitted by the user's brain when carrying out specific and intentional actions. This work presents the implementation on an FPGA of a compact convolutional neural network capable of correctly classifying this type of signals.El desarrollo de las interfaces cerebro-computador busca habilitar la comunicación entre humanos y máquinas, principalmente mediante dispositivos capaces de adquirir e interpretar las señales eléctricas emitidas por el cerebro del usuario al realizar acciones concretas e intencionadas. Este trabajo presenta la implementación sobre una FPGA de una red neuronal convolucional compacta capaz de clasificar correctamente este tipo de señales

Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection

Background: Brain-computer interfacing (BCI) applications based on the classification of electroencephalographic (EEG) signals require solving high-dimensional pattern classification problems with such a relatively small number of training patterns that curse of dimensionality problems usually arise. Multiresolution analysis (MRA) has useful properties for signal analysis in both temporal and spectral analysis, and has been broadly used in the BCI field. However, MRA usually increases the dimensionality of the input data. Therefore, some approaches to feature selection or feature dimensionality reduction should be considered for improving the performance of the MRA based BCI. Methods: This paper investigates feature selection in the MRA-based frameworks for BCI. Several wrapper approaches to evolutionary multiobjective feature selection are proposed with different structures of classifiers. They are evaluated by comparing with baseline methods using sparse representation of features or without feature selection. Results and conclusion: The statistical analysis, by applying the Kolmogorov-Smirnoff and Kruskal-Wallis tests to the means of the Kappa values evaluated by using the test patterns in each approach, has demonstrated some advantages of the proposed approaches. In comparison with the baseline MRA approach used in previous studies, the proposed evolutionary multiobjective feature selection approaches provide similar or even better classification performances, with significant reduction in the number of features that need to be computed