Reinforcement learning refers to the acquisition of approach or avoidance action tendencies through repeated reward/nonreward feedback. Although much research on reinforcement learning has focused on the striatum, the prefrontal cortex likely modulates this process. Given prior research demonstrating a consistent pattern of lateralized frontal cortical activity in affective responses and approa...

Person identification based on spectral information extracted from the EEG is addressed in this work a problem that has not yet been seen in a signal processing framework. Spectral features are extracted non-parametrically from real EEG data recorded from healthy individuals. Neural network classification is applied on these features using a Learning Vector Quantizer in an attempt to experiment...

Brain Computer Interface (BCI) is often directed at mapping, assisting, or repairing human cognitive or sensory-motor functions. Electroencephalogram (EEG) is a non-invasive method of acquisition brain electrical activities. Noises are impure the EEG recorded signal due to the physiologic and extra-physiologic artifacts. There are several techniques are intended to manipulate the EEG recorded s...

In this article we present the results of our research related to the study of correlations between specific visual stimulation and the elicited brain’s electro-physiological response collected by EEG sensors from a group of participants. We will look at how the various characteristics of visual stimulation affect the measured electro-physiological response of the brain and describe the optimal...

We present a data set collected since 2012 containing children’s EEG signals logged during their usage of Project LISTEN’s Reading Tutor. We also present EEG-ML, an integrated machine learning toolkit to preprocess EEG data, extract and select features, train and cross-validate classifiers to predict behavioral labels, and analyze their statistical reliability. To illustrate, we describe and ev...

This study investigates the feasibility of Bens Spike Algorithm (BSA) to encode continuous EEG spatio-temporal data into input spike streams for a classification in a spiking neural network classifier. A novel evolving probabilistic spiking neural network reservoir (epSNNr) architecture is used for the purpose of learning and classifying the EEG signals after the BSA transformation. Experiments...