IMPROVED DEEP LEARNING ARCHITECTURE WITH BATCH NORMALIZATION FOR EEG SIGNAL PROCESSING

Deep learning is commonly used to solve problems such as biomedical problems and many other problems. The most common architecture used to solve those problems is Convolutional Neural Network (CNN) architecture. However, CNN may be prone to overfitting, and the convergence may be slow. One of the methods to overcome the overfitting is batch normalization (BN). BN is commonly used after the convolutional layer. In this study, we proposed a further usage of BN in CNN architecture. BN is not only used after the convolutional layer but also used after the fully connected layer. The proposed architecture is tested to detect types of seizures based on EEG signals. The data used are several sessions of recording signals from many patients. Each recording session produces a recorded EEG signal. EEG signal in each session is first passed through a bandpass filter. Then 26 relevant channels are taken, cut every 2 seconds to be labeled the type of epileptic seizure. The truncated signal is concatenated with the truncated signal from other sessions, divided into two datasets, a large dataset, and a small dataset. Each dataset has four types of seizures. Each dataset is equalized using the undersampling technique. Each dataset is then divided into test and train data to be tested using the proposed architecture. The results show the proposed architecture achieves 46.54% accuracy for the large dataset and 93.33% accuracy for the small dataset. In future studies, the batch normalization parameter will be further investigated to reduce overfitting

Classification of Frequency and Phase Encoded Steady State Visual Evoked Potentials for Brain Computer Interface Speller Applications using Convolutional Neural Networks

Over the past decade there have been substantial improvements in vision based Brain-Computer Interface (BCI) spellers for quadriplegic patient populations. This thesis contains a review of the numerous bio-signals available to BCI researchers, as well as a brief chronology of foremost decoding methodologies used to date. Recent advances in classification accuracy and information transfer rate can be primarily attributed to time consuming patient specific parameter optimization procedures. The aim of the current study was to develop analysis software with potential ‘plug-in-and-play’ functionality. To this end, convolutional neural networks, presently established as state of the art analytical techniques for image processing, were utilized. The thesis herein defines deep convolutional neural network architecture for the offline classification of phase and frequency encoded SSVEP bio-signals. Networks were trained using an extensive 35 participant open source Electroencephalographic (EEG) benchmark dataset (Department of Bio-medical Engineering, Tsinghua University, Beijing). Average classification accuracies of 82.24% and information transfer rates of 22.22 bpm were achieved on a BCI naïve participant dataset for a 40 target alphanumeric display, in absence of any patient specific parameter optimization

Analyzing P300 Distractors for Target Reconstruction

P300-based brain-computer interfaces (BCIs) are often trained per-user and per-application space. Training such models requires ground truth knowledge of target and non-target stimulus categories during model training, which imparts bias into the model. Additionally, not all non-targets are created equal; some may contain visual features that resemble targets or may otherwise be visually salient. Current research has indicated that non-target distractors may elicit attenuated P300 responses based on the perceptual similarity of these distractors to the target category. To minimize this bias, and enable a more nuanced analysis, we use a generalized BCI approach that is fit to neither user nor task. We do not seek to improve the overall accuracy of the BCI with our generalized approach; we instead demonstrate the utility of our approach for identifying target-related image features. When combined with other intelligent agents, such as computer vision systems, the performance of the generalized model equals that of the user-specific models, without any user specific data.Comment: 4 pages, 3 figure

Neonatal Seizure Detection using Convolutional Neural Networks

This study presents a novel end-to-end architecture that learns hierarchical representations from raw EEG data using fully convolutional deep neural networks for the task of neonatal seizure detection. The deep neural network acts as both feature extractor and classifier, allowing for end-to-end optimization of the seizure detector. The designed system is evaluated on a large dataset of continuous unedited multi-channel neonatal EEG totaling 835 hours and comprising of 1389 seizures. The proposed deep architecture, with sample-level filters, achieves an accuracy that is comparable to the state-of-the-art SVM-based neonatal seizure detector, which operates on a set of carefully designed hand-crafted features. The fully convolutional architecture allows for the localization of EEG waveforms and patterns that result in high seizure probabilities for further clinical examination.Comment: IEEE International Workshop on Machine Learning for Signal Processin

Robust asynchronous control of ERP-Based brain-Computer interfaces using deep learning

Producción CientíficaBackground and Objective. Brain-computer interfaces (BCI) based on event-related potentials (ERP) are a promising technology for alternative and augmented communication in an assistive context. However, most approaches to date are synchronous, requiring the intervention of a supervisor when the user wishes to turn his attention away from the BCI system. In order to bring these BCIs into real-life applications, a robust asynchronous control of the system is required through monitoring of user attention. Despite the great importance of this limitation, which prevents the deployment of these systems outside the laboratory, it is often overlooked in research articles. This study was aimed to propose a novel method to solve this problem, taking advantage of deep learning for the first time in this context to overcome the limitations of previous strategies based on hand-crafted features. Methods. The proposed method, based on EEG-Inception, a novel deep convolutional neural network, divides the problem in 2 stages to achieve the asynchronous control: (i) the model detects user’s control state, and (ii) decodes the command only if the user is attending to the stimuli. Additionally, we used transfer learning to reduce the calibration time, even exploring a calibration-less approach. Results. Our method was evaluated with 22 healthy subjects, analyzing the impact of the calibration time and number of stimulation sequences on the system’s performance. For the control state detection stage, we report average accuracies above 91% using only 1 sequence of stimulation and 30 calibration trials, reaching a maximum of 96.95% with 15 sequences. Moreover, our calibration-less approach also achieved suitable results, with a maximum accuracy of 89.36%, showing the benefits of transfer learning. As for the overall asynchronous system, which includes both stages, the maximum information transfer rate was 35.54 bpm, a suitable value for high-speed communication. Conclusions. The proposed strategy achieved higher performance with less calibration trials and stimulation sequences than former approaches, representing a promising step forward that paves the way for more practical applications of ERP-based spellers.Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación (grants PID2020-115468RB-I00 and RTC2019-007350-1)Comisión Europea - Fondo Europeo de Desarrollo Regional (cooperation programme Interreg V-A Spain-Portugal POCTEP 2014–2020