Single ended (SE) amplifiers allow implementing biopotential front-ends with a reduced number of parts, being well suited for preamplified electrodes or compact EEG headboxes. On the other hand, given that each channel has independent gain; mismatching between these gains results in poor common-mode rejection ratios (CMRRs) (about 30 dB considering 1% tolerance components). This work proposes a scheme for multichannel EEG acquisition systems based on SE amplifiers and a novel digital driven right leg (DDRL) circuit, which overcome the poor CMRR of the front-end stage providing a high common mode reduction at power line frequency (up to 80 dB). A functional prototype was built and tested showing the feasibility of the proposed technique. It provided EEG records with negligible power line interference, even in very aggressive EMI environments.
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http://dx.doi.org/10.1109/TBCAS.2012.2190733 | DOI Listing |
Int J Audiol
January 2025
Department of Neurosciences, Research Group ExpORL, KU Leuven, Leuven, Belgium.
Objective: Auditory-steady state responses (ASSRs) to stimuli modulated by different frequencies may differ between children and adults. These differences in response characteristics or latency may reflect developmental changes. This study investigates age-related differences in response strength, latencies, and hemispheric laterality indices of ASSRs for different modulation frequencies.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia.
Traditional tactile brain-computer interfaces (BCIs), particularly those based on steady-state somatosensory-evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contrast, using transient electrical stimuli offers a promising alternative for generating tactile BCI control signals: somatosensory event-related potentials (sERPs). This study aimed to optimize the performance of a novel electrotactile BCI by employing advanced feature extraction and machine learning techniques on sERP signals for the classification of users' selective tactile attention.
View Article and Find Full Text PDFBrain Sci
December 2024
West China Institute of Children's Brain and Cognition, Chongqing University of Education, Chongqing 400065, China.
Background: Emotions play a crucial role in people's lives, profoundly affecting their cognition, decision-making, and interpersonal communication. Emotion recognition based on brain signals has become a significant challenge in the fields of affective computing and human-computer interaction.
Methods: Addressing the issue of inaccurate feature extraction and low accuracy of existing deep learning models in emotion recognition, this paper proposes a multi-channel automatic classification model for emotion EEG signals named DACB, which is based on dual attention mechanisms, convolutional neural networks, and bidirectional long short-term memory networks.
Brain Sci
November 2024
Department of Neurology, Beth Isreal Deaconess Medical Center, Harvard Medical School, Harvard University, Cambridge, MA 02215, USA.
: Manually labeling sleep stages is time-consuming and labor-intensive, making automatic sleep staging methods crucial for practical sleep monitoring. While both single- and multi-channel data are commonly used in automatic sleep staging, limited research has adequately investigated the differences in their effectiveness. In this study, four public data sets-Sleep-SC, APPLES, SHHS1, and MrOS1-are utilized, and an advanced hybrid attention neural network composed of a multi-branch convolutional neural network and the multi-head attention mechanism is employed for automatic sleep staging.
View Article and Find Full Text PDFFront Neuroinform
December 2024
Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.
Introduction: Modeling multi-channel electroencephalographic (EEG) time-series is a challenging tasks, even for the most recent deep learning approaches. Particularly, in this work, we targeted our efforts to the high-fidelity reconstruction of this type of data, as this is of key relevance for several applications such as classification, anomaly detection, automatic labeling, and brain-computer interfaces.
Methods: We analyzed the most recent works finding that high-fidelity reconstruction is seriously challenged by the complex dynamics of the EEG signals and the large inter-subject variability.
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