Narrow Results

Achieving recognizable visual event-related potentials plays an important role in improving the success rate in telepresence control of a humanoid robot via N200 or P300 potentials. The aim of this research is to intensively investigate ways to induce N200 potentials with obvious features by flashing robot images (images with meaningful information) and by flashing pictures containing only solid color squares (pictures with incomprehensible information). Comparative studies have shown that robot images evoke N200 potentials with recognizable negative peaks at approximately 260ms in the frontal and central areas. The negative peak amplitudes increase, on average, from $1.2\mu$V, induced by flashing the squares, to $6.7\mu$V, induced by flashing the robot images. The data analyses support that the N200 potentials induced by the robot image stimuli exhibit recognizable features. Compared with the square stimuli, the robot image stimuli increase the average accuracy rate by 9.92%, from 83.33% to 93.25%, and the average information transfer rate by 24.56bits/min, from 72.18bits/min to 96.74 bits/min, in a single repetition. This finding implies that the robot images might provide the subjects with more information to understand the visual stimuli meanings and help them more effectively concentrate on their mental activities.

The linearly-constrained minimum-variance (LCMV) beamformer is traditionally used as a spatial filter for source localization, but here we consider its spatiotemporal extension for P300 classification. We compare two variants and show that the spatiotemporal LCMV beamformer is at par with state-of-the-art P300 classifiers, but several orders of magnitude faster in training the classifier.

The mechanism of action of vagus nerve stimulation (VNS) is yet to be elucidated. To that end, the effects of VNS on the brain of epileptic patients were studied. Both when VNS was switched “On” and “Off”, the brain activity of responders (R, seizure frequency reduction of over 50%) was compared to the brain activity of nonresponders (NR, seizure frequency reduction of less than 50%). Using EEG recordings, a significant increase in P300 amplitude for R and a significant decrease in P300 amplitude for NR were found. We found biomarkers for checking the efficacy of VNS with accuracy up to 94%. The results show that P300 features recorded in nonmidline electrodes are better P300 biomarkers for VNS efficacy than P300 features recorded in midline electrodes. Using source localization and connectivity analyses, the activity of the limbic system, insula and orbitofrontal cortex was found to be dependent on VNS switched “On” versus “Off” or patient group (R versus NR). The results suggest an important role for these areas in the mechanism of action of VNS, although a larger patient study should be done to confirm the findings.

Increasing command generation rate of an event-related potential-based brain-robot system is challenging, because of limited information transfer rate of a brain-computer interface system. To improve the rate, we propose a dual stimuli approach that is flashing a robot image and is scanning another robot image simultaneously. Two kinds of event-related potentials, N200 and P300 potentials, evoked in this dual stimuli condition are decoded by a convolutional neural network. Compared with the traditional approaches, this proposed approach significantly improves the online information transfer rate from 23.0 or 17.8 to 39.1 bits/min at an accuracy of 91.7%. These results suggest that combining multiple types of stimuli to evoke distinguishable ERPs might be a promising direction to improve the command generation rate in the brain-computer interface.

Traditional training methods need to collect a large amount of data for every subject to train a subject-specific classifier, which causes subjects fatigue and training burden. This study proposes a novel training method, TrAdaBoost based on cross-validation and an adaptive threshold (CV-T-TAB), to reduce the amount of data required for training by selecting and combining multiple subjects’ classifiers that perform well on a new subject to train a classifier. This method adopts cross-validation to extend the amount of the new subject’s training data and sets an adaptive threshold to select the optimal combination of the classifiers. Twenty-five subjects participated in the N200- and P300-based brain–computer interface. The study compares CV-T-TAB to five traditional training methods by testing them on the training of a support vector machine. The accuracy, information transfer rate, area under the curve, recall and precision are used to evaluate the performances under nine conditions with different amounts of data. CV-T-TAB outperforms the other methods and retains a high accuracy even when the amount of data is reduced to one-third of the original amount. The results imply that CV-T-TAB is effective in improving the performance of a subject-specific classifier with a small amount of data by adopting multiple subjects’ classifiers, which reduces the training cost.

Alzheimer’s disease (AD) is the most common cause of dementia that involves a progressive and irrevocable decline in cognitive abilities and social behavior, thus annihilating the patient’s autonomy. The theoretical assumption that disease-modifying drugs are most effective in the early stages hopefully in the prodromal stage called mild cognitive impairment (MCI) urgently pushes toward the identification of robust and individualized markers of cognitive decline to establish an early pharmacological intervention. This requires the combination of well-established neural mechanisms and the development of increasingly sensitive methodologies. Among the neurophysiological markers of attention and cognition, one of the sub-components of the ‘cognitive brain wave’ P300 recordable in an odd-ball paradigm -namely the P3b- is extensively regarded as a sensitive indicator of cognitive performance. Several studies have reliably shown that changes in the amplitude and latency of the P3b are strongly related to cognitive decline and aging both healthy and pathological. Here, we used a P3b spatial filter to enhance the electroencephalographic (EEG) characteristics underlying 175 subjects divided into 135 MCI subjects, 20 elderly controls (EC), and 20 young volunteers (Y). The Y group served to extract the P3b spatial filter from EEG data, which was later applied to the other groups during resting conditions with eyes open and without being asked to perform any task. The group of 135 MCI subjects could be divided into two subgroups at the end of a month follow-up: 75 with stable MCI (MCI-S, not converted to AD), 60 converted to AD (MCI-C). The P3b spatial filter was built by means of a signal processing method called Functional Source Separation (FSS), which increases signal-to-noise ratio by using a weighted sum of all EEG recording channels rather than relying on a single, or a small sub-set, of channels.

A clear difference was observed for the P3b dynamics at rest between groups. Moreover, a machine learning approach showed that P3b at rest could correctly distinguish MCI from EC (80.6% accuracy) and MCI-S from MCI-C (74.1% accuracy), with an accuracy as high as 93.8% in discriminating between MCI-C and EC. Finally, a comparison of the Bayes factor revealed that the group differences among MCI-S and MCI-C were 138 times more likely to be detected using the P3b dynamics compared with the best performing single electrode (Pz) approach.

In conclusion, we propose that P3b as measured through spatial filters can be safely regarded as a simple and sensitive marker to predict the conversion from an MCI to AD status eventually combined with other non-neurophysiological biomarkers for a more precise definition of dementia having neuropathological Alzheimer characteristics.

Human performance is strongly influenced by the sequence of events. Decreasing the response-stimulus interval (RSI) between events qualitatively changes these so-called sequential effects. Using event-related brain potentials (ERPs) to detect electrical brain activity related to sequential patterns helps to uncover mechanisms underlying the observed performance data. Using a spatial compatible two-choice task ERPs were recorded from 32 electrode sites and Independent Component Analysis (ICA) applied to separate sequence-sensitive ERP components from two experiments, involving different RSIs. Independent Component Analysis was able to separate temporally and spatially overlapping ERP components. Sensitivity to the sequence of preceding events could be revealed in an early subcomponent of the N100 complex. Moreover, and in line with earlier reports sequential effects were also observed in P300 subcomponents.

The P300 is an endogenously evoked potential with amplitude and latency depending on the amount of information carried by the stimulus rather than its physical characteristics. It has been suggested that P300 is a manifestation of the context updating mechanism in the human working memory. We present a neural network-based model that mimics the learning and forgetting mechanisms of external stimuli in the human working memory that are believed to be responsible for P300 generation. A modified version of the Hebbian learning rule has been devised to govern the weight dynamics of the network. The model was validated by comparing the characteristics of simulated P300 with actual experimental findings such as the relationship between P300 amplitude and stimulus probability, and task relevance. The results show that the proposed P300 model mimics many aspects of the nervous system responsible for P300 generation.

The current study investigated the time course of the other-race advantage (ORCA) in the subordinate classification of faces and isolated eyes by race. A significant ORCA was found on RTs to both full faces and isolated eyes and faces were classified faster and more accurate than eyes. The ERP data showed that for both stimuli the categorization processes follow basic level classification of physiognomic stimuli, which is not influenced by the stimulus race. The most conspicuous difference between own-race and other-race stimuli as well as between faces and isolated eyes was found in the modulation of the P3 component. The overall pattern of these modulations suggests that the classification of own-race faces is delayed. Since the amplitude of the P3 is sensitive primarily to the perceptual demands of a task, these data suggest that the delay of the own-race classification is caused by an own-race specific process that precedes or interferes with the subordinate classification.

A robotic arm is a mechanical device with a given number of Degrees of Freedom (DoFs) that mimics the functions of a human arm and performs any desired task, such as grasping and moving objects. Current research is directed toward the design of robots and artificial human body parts controlled by brain signals, translating human thoughts into actions. Brain-Computer Interface (BCI) systems have been used to enable people with motor disabilities to control assistive robotic equipment that replaces the lost functions. This paper presents a review of the state-of-the-art of the latest papers dealing with the control of a robotic arm based on Electroencephalogram (EEG). A comparative study of the different methods and techniques used in different blocks of the robotic arm’s noninvasive BCI controlling system is conducted. These blocks include signal acquisition using noninvasive electrodes, signal preprocessing, feature extraction, classification, and command. Additionally, this paper presents a performance comparison of the reviewed controlling systems of robotic arms using EEG signals.

Electrophysiological correlates of human reliability in visual response tasks were investigated in 16 healthy subjects using electroencephalographic (EEG) spectral power and event-related potentials (ERP). Human reliability was first determined by calculating individual reaction accuracy in order to split the entire group into high reliability (HR) and low reliability (LR) subgroups, each with eight subjects. The EEG activities of testing subjects were measured at rest condition for 5 min, and during a modified Eriksen flanker task. Artifact-free EEG segments were used to compute the distribution of EEG at varied frequency bands as well as to detect peak and latency of ERPs of the flanker task. Our results showed that subjects with LR exhibited higher alpha band EEG power at the frontal recording site. Additionally, LR group revealed lower P300 amplitude and predominantly longer P300 latency at centro-parietal recording site than those of the HR group. These findings implied that higher alpha band EEG power at frontal and smaller amplitude, longer latency P300 component of ERP measures at centro-parietal might reveal the trait of lower reliability in healthy controls during visual tasks.

The advancements in the field of brain–computer interface (BCI) are driven by the underlying motive of improving quality of life for both healthy as well as locked in subjects. Since BCI’s are based on the response of the human brain to training or external stimuli, the improvement in terms of performance can be achieved by either enhancing the subject training procedure or by improving the external stimuli to produce maximized event related potential (ERP). P300 and steady-state visually evoked potential (SSVEP) approaches have been the most common paradigms used for stimulus-based BCI’s world over. But recently, a large number of researchers are facing a problem of BCI illiteracy in subjects, where some of the subjects showed ineffective results while training with these BCI as independent stimuli. The concept of hybrid brain–computer interface (hBCI) is a step towards eradicating this problem. Our research deals with external stimuli-based ERP generation where we discuss and compare with experimentation, three different options of visual stimulus: conventional SSVEP stimulus, P300-SSVEP hybrid stimulus, distinct target colors for P300-SSVEP-based hybrid stimulus. This paper introduces a novel hBCI paradigm and discusses the validation of improved results by comparing with the already existing stimuli options. The parameters of comparison that were considered to validate our proposal were decision accuracy (Acc), information transfer rate (ITR) and false activation rate (FAR).

Hybrid brain–computer interfacing (BCI), recently, has been the epicenter of research in the area of rehabilitation engineering. The concept is based on the principle that the paradigm used for the BCI elicits one BCI marker in combination with one or more BCI modalities or other physiological signals. These paradigms elicit human brain response to successfully determine user intentions. Steady-state visually evoked potential (SSVEP) has been the favourite amongst researchers to combine with other BCI modalities such as P300, Motor Imagery (MI), etc. to develop assistive devices (ADs) based on hybrid BCI. This research paper is a record of a comparative study conducted between two hybrid BCI’s, namely hybrid BCI-1, hybrid BCI-2 and traditional SSVEP BCI. Both hybrid paradigms are similar in schematics but differ in the operational protocol. The study aimed to find the optimal protocol which greatly enhances the average information transfer rate (ITR) of a BCI-based AD. Hybrid BCI-1 showed lower classification accuracy (90.36%) and higher false activation rate (FAR) (3.16%) as compared to Hybrid BCI-2 (92.35% and 2.78%, respectively) as well as traditional SSVEP (93.38% and 2.73%, respectively). However, the average ITR of Hybrid BCI-1 (80.76 bits/min) was much higher than that of Hybrid BCI-2 (41.21 bits/min) and traditional SSVEP paradigm (36.34 bits/min). This led to the conclusion, that Hybrid BCI-1 is the most viable option for developing an AD.

This paper presents and compares two approaches for brain computer interface to steer a wheelchair, namely a new visual based P300 paradigm consisting of 8 arrows randomly intensified used for direction selection and a motor imagery paradigm for discrimination of three commands. Classification follows Bayesian and Fisher Linear Discriminant approaches both based on prior statistical knowledge.

Results in P300 paradigm reached false positive and false negative classification accuracies above 90%. Motor imagery experiments presented about 70% accuracy for left vs. right imagery and imagery vs. non-imagery.

Despite extensive experimental investigations of human amnesia, the basic nature of this vivid syndrome remains surrounded by controversy. The dynamics of amnesia, the rapid, selective and long-lasting plasticity of hippocampal synapses, and the connections between the hippocampal formation and association neocortex. all suggest that amnesia may result from damage to the medial temporal site where the recent declarative memory trace is temporarily laid down. Alternatively, amnesics' preserved capacity for procedural learning on indirect memory tests suggests that their deficit may rather be in intentional, sustained and directed (i.e., active) encoding/retrieval processes. It has been difficult to distinguish between these possibilities because amnesics are most impaired on direct memory tasks that involve both a new integrative trace and active processes. It is possible that different amnesics may have a relatively greater defect either in the memory trace, or in active memory processes, or both, and these differences could correspond to differences in their anatomical lesions. Specifically, hippocampal formation lesions may disrupt all recent declarative memory traces, whereas brainstem lesions could produce amnesia by impairing modulatory processes essential for encoding/retrieval or for storage. In this model, the different areas of association neocortex with bidirectional hippocampal connections would contribute specificity to encoding/retrieval, with posterior areas encoding the sensory/semantic aspects of events, and prefrontal cortex the ongoing context. Active modulatory processes arising in the brainstem would then function to integrate this extensive declarative memory system. The cognitive correlates and neural substrates of the evoked potentials recorded during declarative memory tasks suggest that they may embody such modulatory processes. Finally, since the prefrontal cortex and the medial temporal lobe appear to control the onset, intensity and duration of the ascending neuromodulation, lesions of these structures may impair aspects of both the trace and of the processes supporting declarative memory. In summary, a model is proposed in which the association neocortex (encoding/retrieval) and hippocampus (trace) are integrated by the brainstem (modulation) to produce the psychological properties of declarative memory.