Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end-to-end EEG analysis, but a better understanding of how to design and train ConvNets for end-to-end EEG decoding and how to visualize the informative EEG features the ConvN...

The nucleus basalis (NB) mediates cortical electroencephalograph (EEG) activation; NB stimulation also modulates cortical responses to sensory stimuli and can induce learning-related receptive field plasticity. However, little is known about the behavioral effects of NB stimulation. This study concerns the effects of NB stimulation on cardiac and respiratory behavior and quantifies its EEG effe...

Ensemble learning for improving weak classifiers is one important direction in the current research of machine learning, and thereinto bagging, boosting and random subspace are three powerful and popular representatives. They have so far shown efficacies in many practical classification problems. However, for electroencephalogram (EEG) signal classification with application to brain–computer in...

We discuss the physiological metrics that can be measured with electroencephalography (EEG) and functional near infrared spectroscopy (fNIRs). We address the functional and practical limitations of each device, and technical issues to be mindful of when combining the devices. We also present machine learning methods that can be used on concurrent recordings of EEG and fNIRs data. We discuss an ...

Human memory function can be assayed in real-time by electroencephalographic (EEG) recording; however, the clinical utility of this method is dependent on the reliable determination of functionally and diagnostically relevant features. Data-driven machine learning approaches capable of modeling non-stationary signal have been explored as a way to synthesize large arrays of EEG data. Although st...

Abstract Electroencephagram (EEG) is the recording of electrical activity of the brain. Though it is intended to record cerebral signals,it also records the signals that are not of cerebral origin called artifacts. Artifact removal from EEG signals is essential for better diagnosis. This paper proposes a hybrid learning algorithm based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for elimin...

This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate spatial and temporal modeling is required. Inter-channels links are taken into...

Deep learning, and in particular Deep Belief Network (DBN), has recently witnessed increased attention from researchers as a new classification platform. It has been successfully applied to a number of classification problems, such as image classification, speech recognition and natural language processing. However, deep learning has not been fully explored in electroencephalogram (EEG) classif...

Nowadays, deep learning and convolutional neural networks (CNNs) have become widespread tools in many biomedical engineering studies. CNN is an end-to-end tool which makes processing procedure integrated, but in some situations, this processing tool requires to be fused with machine learning methods to be more accurate. In this paper, a hybrid approach based on deep features extracted from Wave...

Prediction of memory performance (remembered or forgotten) has various potential applications not only for knowledge learning but also for disease diagnosis. Recently, subsequent memory effects (SMEs)-the statistical differences in electroencephalography (EEG) signals before or during learning between subsequently remembered and forgotten events-have been found. This finding indicates that EEG ...