How do human children come to understand the actions of other people? What neural systems are associated with the processing of others' actions and how do these systems develop, starting in infancy? These questions span cognitive psychology and developmental cognitive neuroscience, and addressing them has important implications for the study of social cognition. A large amount of research has u...

Independent component analysis (ICA) has proven useful for modeling brain and electroencephalographic (EEG) data. Here, we present a new, generalized method to better capture the dynamics of brain signals than previous ICA algorithms. We regard EEG sources as eliciting spatio-temporal activity patterns, corresponding to, e.g. trajectories of activation propagating across cortex. This leads to a...

A brain-computer interface is a device that uses signals recorded from the brain to directly control a computer. In the last few years, P300-based braincomputer interfaces (BCIs) have proven an effective and reliable means of communication for people with severe motor disabilities such as amyotrophic lateral sclerosis (ALS). Despite this fact, relatively few individuals have benefited from curr...

Signals from eye movements and blinks can be orders of magnitude larger than braingenerated electrical potentials and are one of the main sources of artifacts in electroencephalographic (EEG) data. This article presents a method based on blind source separation (BSS) for automatic removal of electroocular artifacts from EEG datain amotor imagery experiment. BBS is a signalprocessing methodology...

An empathic collaborative robot (cobot) was realized through the transmission of fear from a human agent to agent. Such empathy induced an electroencephalographic (EEG) sensor worn by agent, thus realizing safe brain-computer interface (BCI). The cobot reacts and in turn transmits it forming social circle safety. A first randomized, controlled experiment involved two groups 50 healthy subjects ...

The increase in computer power of the last few decades has allowed the resurgence of the theory behind spatial filtering (a.k.a. beamforming) and its application to array signal processing. That is the case of electroencephalographic (EEG) and magnetoencephalographic (MEG) data, which rely in dense arrays of detectors in order to measure the brain activity noninvasively. In particular, spatial ...

Artifact rejection plays a key role in many signal processing applications. The artifacts are disturbance that can occur during the signal acquisition and that can alter the analysis of the signals themselves. Our aim is to automatically remove the artifacts, in particular from the Electroencephalographic (EEG) recordings. A technique for the automatic artifact rejection, based on the Independe...

In this paper, we analyze the performance of Time Delay Neural Networks (TDNN) and Hidden Markov Models (HMM) for Electroencephalogram (EEG) signal classification. The specific focus of this study is Brain-Computer Interfacing (BCI), where near-real time detection of mental commands during a multi-channel EEG recording is desired. We argue that HMM and TDNN should be preferred over the rigid, o...

Brain Computer Interfaces (BCI) can be used for therapeutic purposes to improve voluntary motor control that has been affected post stroke. For this purpose, desynchronization of sensorimotor rhythms of the electroencephalographic signal (EEG) can be used. But it is necessary to study what happens in the affected motor cortex of this people. In this article, we analyse EEG recordings of hemiple...