Classifying multi-level stress responses from brain cortical EEG in Nurses and Non-health professionals using Machine Learning Auto Encoder

ObjectiveMental stress is a major problem in our society and has become an area of interest for many psychiatric researchers. One primary research focus area is the identification of bio-markers that not only identify stress but also predict the conditions (or tasks) that cause stress. Electroencephalograms (EEGs) have been used for a long time to study and identify bio-markers. While these bio-markers have successfully predicted stress in EEG studies for binary conditions, their performance is suboptimal for multiple conditions of stress.MethodsTo overcome this challenge, we propose using latent based representations of the bio-markers, which have been shown to significantly improve EEG performance compared to traditional bio-markers alone. We evaluated three commonly used EEG based bio-markers for stress, the brain load index (BLI), the spectral power values of EEG frequency bands (alpha, beta and theta), and the relative gamma (RG), with their respective latent representations using four commonly used classifiers.ResultsThe results show that spectral power value based bio-markers had a high performance with an accuracy of 83%, while the respective latent representations had an accuracy of 91%

A low-cost computational method for characterizing event-related potentials for BCI applications and beyond

Event-related potentials (ERPs) are important neurophysiological markers widely used in scientific, medical and engineering contexts. Proper ERP detection contributes to widening the scope of use and, in general, improving functionality. The morphology and latency of ERPs are variable among subject sessions, which complicates their detection. Although variability is an intrinsic feature of neuronal activity, it can be addressed with novel views on ERP detection techniques. In this paper, we propose an agile method for characterizing and thus detecting variable ERPs, which keeps track of their temporal and spatial information through the continuous measurement of the area under the curve in ERP components. We illustrate the usefulness of the proposed ERP characterization for electrode selection in brain-computer interfaces (BCIs) and compare the results with other standard methods. We assess ERP classification for BCI use with Bayesian linear discriminant analysis (BLDA) and cross-validation. We also evaluate performance with both the information transfer rate and BCI utility. The results of our validation tests show that this characterization helps to take advantage of the information on the evolution of positive and negative ERP components and, therefore, to efficiently select electrodes for optimized ERP detection. The proposed method improves the classification accuracy and bitrate of all sets of electrodes analyzed. Furthermore, the method is robust between different day sessions. Our work contributes to the efficient detection of ERPs, manages inter- and intrasubject variability, decreases the computational cost of classic detection methods and contributes to promoting low-cost personalized brain-computer interfaces.This work was supported by the Predoctoral Research Grants of the Ecuador Government through of the Secretaría de Educación Superior, Ciencia, Tecnología e Innovación (SENESCYT) under Grant 2015-AR2Q9086, and by the Ministerio de Ciencia, Innovación y Universidades/FEDER under the Spanish Government Grants: TIN2017-84452-R, DPI2015-65833-P and PGC2018-095895-B-I00

Klasifikasi Eeg Alkoholik Menggunakan Wavelet Packet Decomposition, Principal Component Analysis, Dan Kombinasi Genetic Algorithm Dan Neural Network

Alkoholisme adalah gangguan yang ditandai oleh konsumsi berlebihan dan ketergantungan pada alkohol. Terdapat bermacam cara untuk mendeteksi apakah seorang pasien telah kecanduan alkohol, salah satunya dengan deteksi otak menggunakan Electroencephalographic (EEG). Sinyal EEG secara luas dan klinis digunakan untuk melakukan deteksi gangguan otak pada dunia kesehatan. Akan tetapi, sinyal yang dihasilkan oleh EEG perlu dipersiapkan untuk dilakukan proses agar dapat mendeteksi kelainan otak secara otomatis. Oleh karena itu, perlu adanya metode praproses untuk ekstraksi fitur yang tepat agar mendapatkan karakteristik yang tersimpan secara implisit dari sinyal EEG tersebut. Tugas Akhir ini akan mengimplementasikan metode Wavelet Packet Decomposition (WPD) untuk ekstraksi fitur, Principal Component Analysis (PCA) untuk reduksi dimensi, dan Neural Network yang dioptimasi dengan metode Genetic Algorithm dalam pencarian bobot dan bias optimal untuk klasifikasi data alkoholik dan normal. Data uji coba yang digunakan dibagi menjadi 2 yaitu, dataset 1 terdiri dari 60 training dan 40 testing serta dataset 2 terdiri dari 120 training dan 40 testing. Berdasarkan hasil uji coba, rata-rata akurasi terbaik didapatkan dari dataset 1 sebesar 94.00% dengan dekomposisi 3 level, penggunaan fitur 30%, dan klasifikasi menggunakan Kombinasi Neural Network dan Genetic Algorithm dengan learning rate 0.1 dan nilai alpha pada proses crossover 0.9. =============================================================================================== Alcoholism is a disorder characterized by excessive consumption and dependence on alcohol. There are various ways to detect whether a patient is addicted to alcohol, one of them by brain detection using electroencephalographic (EEG). EEG signals are widely and clinically used for detection of brain disorders in the medical world. However, the signals generated by the EEG should be prepared to do further processing to detect brain abnormalities automatically. Initially, preprocessing method is needed for proper feature extraction, in order to obtain characteristics that are stored implicitly in the EEG signal. This undergraduate thesis implements Wavelet Packet Decomposition (WPD) method for feature extraction, Principal Component Analysis (PCA) for dimension reduction, and Back Propagation Neural Network optimized with Genetic Algorithm to get optimal weights and biases for alcohol addiction classification. The EEG data used are divided into two: the first dataset consists of 60 training data and 40 testing data, and the second dataset consists of 120 training data and 40 testing data. Based on the experiment results, the first dataset gives best performance with 94.00% accuracy with decomposition of 3 levels, taking 30% of the features, and classification using Neural Network and Genetic Algorithm with learning rate of 0.1 and crossover alpha value of 0.9

Towards smarter Brain Computer Interface (BCI): study of electroencephalographic signal processing and classification techniques toward the use of intelligent and adaptive BCI

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de Lectura: 28-07-202

P300 Detection Based on EEG Shape Features

We present a novel approach to describe a P300 by a shape-feature vector, which offers several advantages over the feature vector used by the BCI2000 system. Additionally, we present a calibration algorithm that reduces the dimensionality of the shape-feature vector, the number of trials, and the electrodes needed by a Brain Computer Interface to accurately detect P300s; we also define a method to find a template that best represents, for a given electrode, the subject’s P300 based on his/her own acquired signals. Our experiments with 21 subjects showed that the SWLDA’s performance using our shape-feature vector was 93%, that is, 10% higher than the one obtained with BCI2000-feature’s vector. The shape-feature vector is 34-dimensional for every electrode; however, it is possible to significantly reduce its dimensionality while keeping a high sensitivity. The validation of the calibration algorithm showed an averaged area under the ROC (AUROC) curve of 0.88. Also, most of the subjects needed less than 15 trials to have an AUROC superior to 0.8. Finally, we found that the electrode C4 also leads to better classification