2,619 research outputs found
Revealing spatio-spectral electroencephalographic dynamics of musical mode and tempo perception by independent component analysis.
BackgroundMusic conveys emotion by manipulating musical structures, particularly musical mode- and tempo-impact. The neural correlates of musical mode and tempo perception revealed by electroencephalography (EEG) have not been adequately addressed in the literature.MethodThis study used independent component analysis (ICA) to systematically assess spatio-spectral EEG dynamics associated with the changes of musical mode and tempo.ResultsEmpirical results showed that music with major mode augmented delta-band activity over the right sensorimotor cortex, suppressed theta activity over the superior parietal cortex, and moderately suppressed beta activity over the medial frontal cortex, compared to minor-mode music, whereas fast-tempo music engaged significant alpha suppression over the right sensorimotor cortex.ConclusionThe resultant EEG brain sources were comparable with previous studies obtained by other neuroimaging modalities, such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). In conjunction with advanced dry and mobile EEG technology, the EEG results might facilitate the translation from laboratory-oriented research to real-life applications for music therapy, training and entertainment in naturalistic environments
Discovering Gender Differences in Facial Emotion Recognition via Implicit Behavioral Cues
We examine the utility of implicit behavioral cues in the form of EEG brain
signals and eye movements for gender recognition (GR) and emotion recognition
(ER). Specifically, the examined cues are acquired via low-cost, off-the-shelf
sensors. We asked 28 viewers (14 female) to recognize emotions from unoccluded
(no mask) as well as partially occluded (eye and mouth masked) emotive faces.
Obtained experimental results reveal that (a) reliable GR and ER is achievable
with EEG and eye features, (b) differential cognitive processing especially for
negative emotions is observed for males and females and (c) some of these
cognitive differences manifest under partial face occlusion, as typified by the
eye and mouth mask conditions.Comment: To be published in the Proceedings of Seventh International
Conference on Affective Computing and Intelligent Interaction.201
Computational Modeling of Temporal EEG Responses to Cyclic Binary Visual Stimulus Patterns
The human visual system serves as the basis for many modern computer vision and machine learning approaches. While detailed biophysical models of certain aspects of the visual system exist, little work has been done to develop an end-to-end model from the visual stimulus to the signals generated at the visual cortex measured via the scalp electroencephalogram (EEG). The creation of such a model would not only provide a better understanding of the visual processing pathways but would also facilitate the design and evaluation of more robust visual stimuli for brain-computer interfaces (BCIs). A novel experiment was designed and conducted where 15 participants viewed stereotyped visual stimuli while their EEG was recorded simultaneously. The resulting EEG responses were characterized across participants. Furthermore, a Residual Connection Feed Forward system identification Neural Network (ReCon FFNN) was implemented as a preliminary end-to-end model of the visual system that uses the temporal characteristics of the visual stimulus as the model input and the corresponding EEG time series as the model output. This preliminary model was able to reproduce temporal and spectral characteristics of the EEG and serves as a proof of concept for the development of future artificial neural network or biophysical models that incorporate spatio-temporal information
Spatial Filtering Pipeline Evaluation of Cortically Coupled Computer Vision System for Rapid Serial Visual Presentation
Rapid Serial Visual Presentation (RSVP) is a paradigm that supports the
application of cortically coupled computer vision to rapid image search. In
RSVP, images are presented to participants in a rapid serial sequence which can
evoke Event-related Potentials (ERPs) detectable in their Electroencephalogram
(EEG). The contemporary approach to this problem involves supervised spatial
filtering techniques which are applied for the purposes of enhancing the
discriminative information in the EEG data. In this paper we make two primary
contributions to that field: 1) We propose a novel spatial filtering method
which we call the Multiple Time Window LDA Beamformer (MTWLB) method; 2) we
provide a comprehensive comparison of nine spatial filtering pipelines using
three spatial filtering schemes namely, MTWLB, xDAWN, Common Spatial Pattern
(CSP) and three linear classification methods Linear Discriminant Analysis
(LDA), Bayesian Linear Regression (BLR) and Logistic Regression (LR). Three
pipelines without spatial filtering are used as baseline comparison. The Area
Under Curve (AUC) is used as an evaluation metric in this paper. The results
reveal that MTWLB and xDAWN spatial filtering techniques enhance the
classification performance of the pipeline but CSP does not. The results also
support the conclusion that LR can be effective for RSVP based BCI if
discriminative features are available
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