AIMC Topic: Electroencephalography

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Decoding neural activity preceding balance loss during standing with a lower-limb exoskeleton using an interpretable deep learning model.

Journal of neural engineering
Falls are a leading cause of death in adults 65 and older. Recent efforts to restore lower-limb function in these populations have seen an increase in the use of wearable robotic systems; however, fall prevention measures in these systems require ear...

A Data-Driven Adaptive Emotion Recognition Model for College Students Using an Improved Multifeature Deep Neural Network Technology.

Computational intelligence and neuroscience
With the increasing pressure on college students in terms of study, work, emotion, and life, the emotional changes of college students are becoming more and more obvious. For college student management workers, if they can accurately grasp the emotio...

Primary Experiences with Robot-assisted Navigation-based Frameless Stereo-electroencephalography: Higher Accuracy than Neuronavigation-guided Manual Adjustment.

Neurologia medico-chirurgica
The use of robot-assisted frameless stereotactic electroencephalography (SEEG) is becoming more common. Among available robotic arms, Stealth Autoguide (SA) (Medtronic, Minneapolis, MN, USA) functions as an optional instrument of the neuronavigation ...

Short report: surgery for implantable brain-computer interface assisted by robotic navigation system.

Acta neurochirurgica
We present an implantable brain-computer interface surgical case assisted by robotic navigation system in an elderly patient with tetraplegia caused by cervical spinal cord injury. Left primary motor cortex was selected for implantation of microelect...

A Deep Learning Method Approach for Sleep Stage Classification with EEG Spectrogram.

International journal of environmental research and public health
The classification of sleep stages is an important process. However, this process is time-consuming, subjective, and error-prone. Many automated classification methods use electroencephalogram (EEG) signals for classification. These methods do not cl...

A Bimodal Deep Learning Architecture for EEG-fNIRS Decoding of Overt and Imagined Speech.

IEEE transactions on bio-medical engineering
OBJECTIVE: Brain-computer interfaces (BCI) studies are increasingly leveraging different attributes of multiple signal modalities simultaneously. Bimodal data acquisition protocols combining the temporal resolution of electroencephalography (EEG) wit...

Multimodal Vigilance Estimation Using Deep Learning.

IEEE transactions on cybernetics
The phenomenon of increasing accidents caused by reduced vigilance does exist. In the future, the high accuracy of vigilance estimation will play a significant role in public transportation safety. We propose a multimodal regression network that cons...

Spatial-frequency-temporal convolutional recurrent network for olfactory-enhanced EEG emotion recognition.

Journal of neuroscience methods
BACKGROUND: Multimedia stimulation of brain activity is important for emotion induction. Based on brain activity, emotion recognition using EEG signals has become a hot issue in the field of affective computing.

Deep-learning-based seizure detection and prediction from electroencephalography signals.

International journal for numerical methods in biomedical engineering
Electroencephalography (EEG) is among the main tools used for analyzing and diagnosing epilepsy. The manual analysis of EEG must be conducted by highly trained clinicians or neuro-physiologists; a process that is considered to have a comparatively lo...

A neuroscience-inspired spiking neural network for EEG-based auditory spatial attention detection.

Neural networks : the official journal of the International Neural Network Society
Recent studies have shown that alpha oscillations (8-13 Hz) enable the decoding of auditory spatial attention. Inspired by sparse coding in cortical neurons, we propose a spiking neural network model for auditory spatial attention detection. The prop...