1,076 research outputs found
Spatial Detection of Multiple Movement Intentions from SAM-Filtered Single-Trial MEG for a high performance BCI
The objective of this study is to test whether human intentions to sustain or cease movements in right and left hands can be decoded reliably from spatially filtered single trial magneto-encephalographic (MEG) signals. This study was performed using motor execution and motor imagery movements to achieve a potential high performance Brain-Computer interface (BCI). Seven healthy volunteers, naïve to BCI technology, participated in this study. Signals were recorded from 275-channel MEG and synthetic aperture magnetometry (SAM) was employed as the spatial filter. The four-class classification for natural movement intentions was performed offline; Genetic Algorithm based Mahalanobis Linear Distance (GA-MLD) and direct-decision tree classifier (DTC) techniques were adopted for the classification through 10-fold cross-validation. Through SAM imaging, strong and distinct event related desynchronisation (ERD) associated with sustaining, and event related synchronisation (ERS) patterns associated with ceasing of hand movements were observed in the beta band (15 - 30 Hz). The right and left hand ERD/ERS patterns were observed on the contralateral hemispheres for motor execution and motor imagery sessions. Virtual channels were selected from these cortical areas of high activity to correspond with the motor tasks as per the paradigm of the study. Through a statistical comparison between SAM-filtered virtual channels from single trial MEG signals and basic MEG sensors, it was found that SAM-filtered virtual channels significantly increased the classification accuracy for motor execution (GA-MLD: 96.51 ± 2.43 %) as well as motor imagery sessions (GA-MLD: 89.69 ± 3.34%). Thus, multiple movement intentions can be reliably detected from SAM-based spatially-filtered single trial MEG signals. MEG signals associated with natural motor behavior may be utilized for a reliable high-performance brain-computer interface (BCI) and may reduce long-term training compared with conventional BCI methods using rhythm control. This may prove tremendously helpful for patients suffering from various movement disorders to improve their quality of life
Translation of EEG spatial filters from resting to motor imagery using independent component analysis.
Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) often use spatial filters to improve signal-to-noise ratio of task-related EEG activities. To obtain robust spatial filters, large amounts of labeled data, which are often expensive and labor-intensive to obtain, need to be collected in a training procedure before online BCI control. Several studies have recently developed zero-training methods using a session-to-session scenario in order to alleviate this problem. To our knowledge, a state-to-state translation, which applies spatial filters derived from one state to another, has never been reported. This study proposes a state-to-state, zero-training method to construct spatial filters for extracting EEG changes induced by motor imagery. Independent component analysis (ICA) was separately applied to the multi-channel EEG in the resting and the motor imagery states to obtain motor-related spatial filters. The resultant spatial filters were then applied to single-trial EEG to differentiate left- and right-hand imagery movements. On a motor imagery dataset collected from nine subjects, comparable classification accuracies were obtained by using ICA-based spatial filters derived from the two states (motor imagery: 87.0%, resting: 85.9%), which were both significantly higher than the accuracy achieved by using monopolar scalp EEG data (80.4%). The proposed method considerably increases the practicality of BCI systems in real-world environments because it is less sensitive to electrode misalignment across different sessions or days and does not require annotated pilot data to derive spatial filters
Successful object encoding induces increased directed connectivity in presymptomatic early-onset Alzheimer's disease
Background: Recent studies report increases in neural activity in brain regions critical to episodic memory at preclinical stages of Alzheimer’s disease (AD). Although electroencephalography (EEG) is widely used in AD studies, given its non-invasiveness and low cost, there is a need to translate the findings in other neuroimaging methods to EEG.
Objective: To examine how the previous findings using functional magnetic resonance imaging (fMRI) at preclinical stage in presenilin-1 E280A mutation carriers could be assessed and extended, using EEG and a connectivity approach.
Methods: EEG signals were acquired during resting and encoding in 30 normal cognitive young subjects, from an autosomal dominant early-onset AD kindred from Antioquia, Colombia. Regions of the brain previously reported as hyperactive were used for connectivity analysis.
Results: Mutation carriers exhibited increasing connectivity at analyzed regions. Among them, the right precuneus exhibited the highest changes in connectivity.
Conclusion: Increased connectivity in hyperactive cerebral regions is seen in individuals, genetically-determined to develop AD, at preclinical stage. The use of a connectivity approach and a widely available neuroimaging technique opens the possibility to increase the use of EEG in early detection of preclinical AD.Postprint (author's final draft
Pre-stimulus influences on auditory perception arising from sensory representations and decision processes
The qualities of perception depend not only on the sensory inputs but also on the brain state before stimulus presentation. Although the collective evidence from neuroimaging studies for a relation between prestimulus state and perception is strong, the interpretation in the context of sensory computations or decision processes has remained difficult. In the auditory system, for example, previous studies have reported a wide range of effects in terms of the perceptually relevant frequency bands and state parameters (phase/power). To dissociate influences of state on earlier sensory representations and higher-level decision processes, we collected behavioral and EEG data in human participants performing two auditory discrimination tasks relying on distinct acoustic features. Using single-trial decoding, we quantified the relation between prestimulus activity, relevant sensory evidence, and choice in different task-relevant EEG components. Within auditory networks, we found that phase had no direct influence on choice, whereas power in task-specific frequency bands affected the encoding of sensory evidence. Within later-activated frontoparietal regions, theta and alpha phase had a direct influence on choice, without involving sensory evidence. These results delineate two consistent mechanisms by which prestimulus activity shapes perception. However, the timescales of the relevant neural activity depend on the specific brain regions engaged by the respective task
Electromagnetic Source Imaging via a Data-Synthesis-Based Convolutional Encoder-Decoder Network
Electromagnetic source imaging (ESI) requires solving a highly ill-posed
inverse problem. To seek a unique solution, traditional ESI methods impose
various forms of priors that may not accurately reflect the actual source
properties, which may hinder their broad applications. To overcome this
limitation, in this paper a novel data-synthesized spatio-temporally
convolutional encoder-decoder network method termed DST-CedNet is proposed for
ESI. DST-CedNet recasts ESI as a machine learning problem, where discriminative
learning and latent-space representations are integrated in a convolutional
encoder-decoder network (CedNet) to learn a robust mapping from the measured
electroencephalography/magnetoencephalography (E/MEG) signals to the brain
activity. In particular, by incorporating prior knowledge regarding dynamical
brain activities, a novel data synthesis strategy is devised to generate
large-scale samples for effectively training CedNet. This stands in contrast to
traditional ESI methods where the prior information is often enforced via
constraints primarily aimed for mathematical convenience. Extensive numerical
experiments as well as analysis of a real MEG and Epilepsy EEG dataset
demonstrate that DST-CedNet outperforms several state-of-the-art ESI methods in
robustly estimating source signals under a variety of source configurations.Comment: 15 pages, 14 figures, and journa
The neurophysiological basis of short- and long-term ventriloquism aftereffects
Park H, Kayser C. The neurophysiological basis of short- and long-term ventriloquism aftereffects. bioRxiv. 2020.ABSTRACTOur senses often receive conflicting multisensory information, which our brain reconciles by adaptive recalibration. A classic example is the ventriloquist aftereffect, which emerges following both long-term and trial-wise exposure to spatially discrepant multisensory stimuli. Still, it remains debated whether the behavioral biases observed following short- and long-term exposure arise from largely the same or rather distinct neural origins, and hence reflect the same or distinct mechanisms. We address this question by probing EEG recordings for physiological processes predictive of the single-trial ventriloquism biases following the exposure to spatially offset audio-visual stimuli. Our results support the hypothesis that both short- and long-term aftereffects are mediated by common neurophysiological correlates, which likely arise from sensory and parietal regions involved in multisensory inference and memory, while prolonged exposure to consistent discrepancies additionally recruits prefrontal regions. These results posit a central role of parietal regions in mediating multisensory spatial recalibration and suggest that frontal regions contribute to increasing the behavioral bias when the perceived sensory discrepancy is consistent and persistent over time.</jats:p
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