Search Results for author:
Found 11 papers, 1 papers with code
Upper Limb Movement Execution Classification using Electroencephalography for Brain Computer Interface
We propose a method to classify four ME classes for different subjects using spectrograms of the EEG data through pre-trained deep learning (DL) models.
Motor imagery classification using EEG spectrograms
Our novel approach for the classification of upper limb movements using pre-trained DL algorithms and spectrograms has achieved significantly improved results for seven movement classes.
A Multimodal Perceived Stress Classification Framework using Wearable Physiological Sensors
A perceived stress scale (PSS) questionnaire is used to record the stress of participants, which is then used to assign stress labels (two- and three classes).
Human Stress Assessment: A Comprehensive Review of Methods Using Wearable Sensors and Non-wearable Techniques
This paper presents a comprehensive review of methods covering significant subjective and objective human stress detection techniques available in the literature.
Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI data
Alzheimer's disease (AD) is a progressive and incurable neurodegenerative disease which destroys brain cells and causes loss to patient's memory.
Electroencephalography based Classification of Long-term Stress using Psychological Labeling
Stress research is a rapidly emerging area in thefield of electroencephalography (EEG) based signal processing. The use of EEG as an objective measure for cost effective andpersonalized stress management becomes important in particularsituations such as the non-availability of mental health facilities. In this study, long-term stress is classified using baseline EEGsignal recordings.
Emotion Classification in Response to Tactile Enhanced Multimedia using Frequency Domain Features of Brain Signals
The response to this enhanced multimedia content (mulsemedia) is evaluated in terms of the appreciation/emotion by using human brain signals.
Classification of Perceived Human Stress using Physiological Signals
In this paper, we present an experimental study for the classification of perceived human stress using non-invasive physiological signals.
Medical Image Analysis using Convolutional Neural Networks: A Review
Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features.
Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network
A patch based approach along with an inception module is used for training the deep network by extracting two co-centric patches of different sizes from the input images.
Medical Image Retrieval using Deep Convolutional Neural Network
The learned features and the classification results are used to retrieve medical images.
