AIMC Topic: Electrodes

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Machine learning classifiers for electrode selection in the design of closed-loop neuromodulation devices for episodic memory improvement.

Cerebral cortex (New York, N.Y. : 1991)
Successful neuromodulation approaches to alter episodic memory require closed-loop stimulation predicated on the effective classification of brain states. The practical implementation of such strategies requires prior decisions regarding electrode im...

A warm hug from a robot: A dual-mode e-skin with programming compliance.

The Review of scientific instruments
Recent achievements in the field of electronic skin (e-skin) have provided promising technology for service robots. However, the development of a bionic perception system that exhibits superior performance in terms of safety and interaction quality r...

Application of neural network approach for modelling COD reduction from real refinery effluent by electrocoagulation.

Water science and technology : a journal of the International Association on Water Pollution Research
The present study aims to investigate the feasibility of implementing the electrocoagulation (EC) process to treat Algiers refinery effluent. The electrocoagulation was performed by using scrap aluminum plate electrodes in monopolar-parallel mode. Se...

Comparing the Usability of Alternative EEG Devices to Traditional Electrode Caps for SSVEP-BCI Controlled Assistive Robots.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
Despite having the potential to improve the lives of severely paralyzed users, non-invasive Brain Computer Interfaces (BCI) have yet to be integrated into their daily lives. The widespread adoption of BCI-driven assistive technology is hindered by it...

EEG-GAT: Graph Attention Networks for Classification of Electroencephalogram (EEG) Signals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Graph neural networks (GNN) are an emerging framework in the deep learning community. In most GNN applications, the graph topology of data samples is provided in the dataset. Specifically, the graph shift operator (GSO), which could be adjacency, gra...

EEG-based Emotion Recognition Using Graph Convolutional Network with Learnable Electrode Relations.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Emotion recognition based on electroencephalography (EEG) plays a pivotal role in the field of affective computing, and graph convolutional neural network (GCN) has been proved to be an effective method and made considerable progress. Since the adjac...

EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Convolutional neural networks (CNN) have been frequently used to extract subject-invariant features from electroencephalogram (EEG) for classification tasks. This approach holds the underlying assumption that electrodes are equidistant analogous to p...

Impact of Encapsulation Tissue Growth on Selective Recording in Nerve Cuff Electrodes: A Simulation Study.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Peripheral nerve interfaces (PNIs) allow us to extract motor, sensory and autonomic information from the nervous system and use it as control signals in neuroprosthetic and neuromodulation systems. Recent efforts have aimed to improve the recording s...

Partial directed coherence based graph convolutional neural networks for driving fatigue detection.

The Review of scientific instruments
The mental state of a driver can be accurately and reliably evaluated by detecting the driver's electroencephalogram (EEG) signals. However, traditional machine learning and deep learning methods focus on the single electrode feature analysis and ign...

Electroencephalogram-Based Emotion Recognition Using a Particle Swarm Optimization-Derived Support Vector Machine Classifier.

Critical reviews in biomedical engineering
We sort human emotions using Russell's circumplex model of emotion by classifying electroencephalogram (EEG) signals from 25 subjects into four discrete states, namely, happy, sad, angry, and relaxed. After acquiring signals, we use a standard databa...