Error-related scalp potentials elicited by hand and foot movements : evidence for an output-independent error-processing system in humans

The error-related negativity (ERN) is a fronto-centrally distributed component of the event-related brain potential (ERP) that occurs when human subjects make errors in a variety of experimental tasks. In the present study, we recorded ERPs from 128 scalp electrodes while subjects performed a choice reaction time task using either their hands or feet. We applied the brain electric source analysis technique to compare ERNs elicited by hand and foot errors. The scalp distributions of these error potentials suggest that they share the same neural generator and, therefore, that the ERN process is output-independent. Together with other findings, the results are consistent with the hypothesis that the ERN is generated within the anterior cingulate cortex and is elicited by the activation of a generic error-processing system. (C) 1998 Elsevier Science Ireland Ltd

EEG を用いた人間脳活動計測

Abstract：Brain activity measurement has become a useful tool to deepen our understanding of human beings as well as to develop new applications to improve the quality of people’s life. Though EEG (electroencephalogram) has a history of about 90 years in measuring human brain activities, it is still a popular method nowadays in both medical fronts and other research fields, such as neuroscience, psychology, physiology and engineering. The EEG devices have evolved dramatically in different directions to fit the measurement requirements with high spatial resolution or portable convenience. In this paper, the history of the non-invasive human brain activity measurement is looked back briefly. Then the mechanism of EEG and its evolvement are reviewed. Different types of EEG as well as their typical applications are introduced. Finally, the potential prospects of EEG are surveyed.脳活動計測は人間を理解するための有効なツールであり、その応用は生活の質を向上するにも役立つ。EEG は約90 年の歴史を持ちながら、現在でも医療現場や神経科学、心理学、生理学、工学などの研究分野で活用されている。EEG 装置は高い空間分解能や持ち運び便利さなどの要求に応じて、異なる方向に進化してきた。本論文は、人間の非侵襲脳活動計測の歴史を振り返し、EEG の仕組みやその進化を説明する上、異なるタイプのEEG 及び応用を紹介する。最後にEEG の将来を展望する

Time course and robustness of ERP object and face differences

Conflicting results have been reported about the earliest “true” ERP differences related to face processing, with the bulk of the literature focusing on the signal in the first 200 ms after stimulus onset. Part of the discrepancy might be explained by uncontrolled low-level differences between images used to assess the timing of face processing. In the present experiment, we used a set of faces, houses, and noise textures with identical amplitude spectra to equate energy in each spatial frequency band. The timing of face processing was evaluated using face–house and face–noise contrasts, as well as upright-inverted stimulus contrasts. ERP differences were evaluated systematically at all electrodes, across subjects, and in each subject individually, using trimmed means and bootstrap tests. Different strategies were employed to assess the robustness of ERP differential activities in individual subjects and group comparisons. We report results showing that the most conspicuous and reliable effects were systematically observed in the N170 latency range, starting at about 130–150 ms after stimulus onset

Transcranial Electrical Neuromodulation Based on the Reciprocity Principle

A key challenge in multi-electrode transcranial electrical stimulation (TES) or transcranial direct current stimulation (tDCS) is to find a current injection pattern that delivers the necessary current density at a target and minimizes it in the rest of the head, which is mathematically modeled as an optimization problem. Such an optimization with the Least Squares (LS) or Linearly Constrained Minimum Variance (LCMV) algorithms is generally computationally expensive and requires multiple independent current sources. Based on the reciprocity principle in electroencephalography (EEG) and TES, it could be possible to find the optimal TES patterns quickly whenever the solution of the forward EEG problem is available for a brain region of interest. Here, we investigate the reciprocity principle as a guideline for finding optimal current injection patterns in TES that comply with safety constraints. We define four different trial cortical targets in a detailed seventissue finite element head model, and analyze the performance of the reciprocity family of TES methods in terms of electrode density, targeting error, focality, intensity, and directionality using the LS and LCMV solutions as the reference standards. It is found that the reciprocity algorithms show good performance comparable to the LCMV and LS solutions. Comparing the 128 and 256 electrode cases, we found that use of greater electrode density improves focality, directionality, and intensity parameters. The results show that reciprocity principle can be used to quickly determine optimal current injection patterns in TES and help to simplify TES protocols that are consistent with hardware and software availability and with safety constraints.Laboratorio de Electrónica Industrial, Control e Instrumentación (LEICI

One Action System or Two? Evidence for Common Central Preparatory Mechanisms in Voluntary and Stimulus-Driven Actions

Human behavior is comprised of an interaction between intentionally driven actions and reactions to changes in the environment. Existing data are equivocal concerning the question of whether these two action systems are independent, involve different brain regions, or overlap. To address this question we investigated whether the degree to which the voluntary action system is activated at the time of stimulus onset predicts reaction times to external stimuli.Werecorded event-related potentials while participants prepared and executed left- or right-hand voluntary actions, which were occasionally interrupted by a stimulus requiring either a left- or right-hand response. In trials where participants successfully performed the stimulus-driven response, increased voluntary motor preparation was associated with faster responses on congruent trials (where participants were preparing a voluntary action with the same hand that was then required by the target stimulus), and slower responses on incongruent trials. This suggests that early hand-specific activity in medial frontal cortex for voluntary action trials can be used by the stimulus-driven system to speed responding. This finding questions the clear distinction between voluntary and stimulus-driven action systems. © 2011 the authors

Single-trial EEG Discrimination between Wrist and Finger Movement Imagery and Execution in a Sensorimotor BCI

A brain-computer interface (BCI) may be used to control a prosthetic or orthotic hand using neural activity from the brain. The core of this sensorimotor BCI lies in the interpretation of the neural information extracted from electroencephalogram (EEG). It is desired to improve on the interpretation of EEG to allow people with neuromuscular disorders to perform daily activities. This paper investigates the possibility of discriminating between the EEG associated with wrist and finger movements. The EEG was recorded from test subjects as they executed and imagined five essential hand movements using both hands. Independent component analysis (ICA) and time-frequency techniques were used to extract spectral features based on event-related (de)synchronisation (ERD/ERS), while the Bhattacharyya distance (BD) was used for feature reduction. Mahalanobis distance (MD) clustering and artificial neural networks (ANN) were used as classifiers and obtained average accuracies of 65 % and 71 % respectively. This shows that EEG discrimination between wrist and finger movements is possible. The research introduces a new combination of motor tasks to BCI research.Comment: 33rd Annual International IEEE EMBS Conference 201

Neurometric Profiling of Autism Spectrum Disorder using The Brief Neurometric Battery: A Novel Eeg Based Task

Autism spectrum disorder is a pervasive developmental disorder characterized by heterogeneous deficits in social communication and interaction, as well as repetitive behaviors and restricted interests. Due to the dramatic increase in prevalence, a major theme in contemporary research has been the identification of biomarkers for ASD that can shed light on etiological factors, facilitate diagnosis and serve as markers for tracking the efficacy of behavioral and pharmacological treatments. Electroencephalography (EEG) metrics, such as event-related potentials (ERPs), resting state oscillatory activity (OA), and resting state complexity (multiscale entropy), are well-suited for the measurement of such biomarkers. Due to the complexity and heterogeneity of ASD symptoms, it is important that research aiming to use EEG to identify biomarkers of autism and other neurodevelopmental disorders focus on determining the relationships between electrophysiological neurometrics and clinical presentation. The objective of the present research was two-fold; 1) synthesize a profile of ERP and OA metrics, collected during a novel Brief Neurometric Battery, that differentiates between youth with ASD and controls, and 2) determine if a relatively novel analysis of resting state EEG complexity (MSE) can be used to differentiate between ASD and controls. Through a two study approach, this research was able to synthesize a multivariate profile that classified youth with and without ASD at an accuracy rate comparable to that of the gold standard methods (ADI-R/ADOS) and identify an additional neurometric, multiscale entropy, that can accurately differentiate between youth with ASD and controls