AIMC Topic: Brain
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Understanding Learning from EEG Data: Combining Machine Learning and Feature Engineering Based on Hidden Markov Models and Mixed Models.
Neuroinformatics
Sep 10, 2024
Theta oscillations, ranging from 4-8 Hz, play a significant role in spatial learning and memory functions during navigation tasks. Frontal theta oscillations are thought to play an important role in spatial navigation and memory. Electroencephalograp...
Automatic Recognition of Multiple Emotional Classes from EEG Signals through the Use of Graph Theory and Convolutional Neural Networks.
Sensors (Basel, Switzerland)
Sep 10, 2024
Emotion is a complex state caused by the functioning of the human brain in relation to various events, for which there is no scientific definition. Emotion recognition is traditionally conducted by psychologists and experts based on facial expression...
Intelligent Control System for Brain-Controlled Mobile Robot Using Self-Learning Neuro-Fuzzy Approach.
Sensors (Basel, Switzerland)
Sep 10, 2024
Brain-computer interface (BCI) provides direct communication and control between the human brain and physical devices. It is achieved by converting EEG signals into control commands. Such interfaces have significantly improved the lives of disabled i...
Can a Hebbian-like learning rule be avoiding the curse of dimensionality in sparse distributed data?
Biological cybernetics
Sep 9, 2024
It is generally assumed that the brain uses something akin to sparse distributed representations. These representations, however, are high-dimensional and consequently they affect classification performance of traditional Machine Learning models due ...
Siamese based deep neural network for ADHD detection using EEG signal.
Computers in biology and medicine
Sep 9, 2024
BACKGROUND: Detecting Attention-Deficit/Hyperactivity Disorder (ADHD) in children is crucial for timely intervention and personalized treatment.
Enhancing early Parkinson's disease detection through multimodal deep learning and explainable AI: insights from the PPMI database.
Scientific reports
Sep 9, 2024
Parkinson's is the second most common neurodegenerative disease, affecting nearly 8.5M people and steadily increasing. In this research, Multimodal Deep Learning is investigated for the Prodromal stage detection of Parkinson's Disease (PD), combining...
Is artificial consciousness achievable? Lessons from the human brain.
Neural networks : the official journal of the International Neural Network Society
Sep 7, 2024
We here analyse the question of developing artificial consciousness from an evolutionary perspective, taking the evolution of the human brain and its relation with consciousness as a reference model or as a benchmark. This kind of analysis reveals se...
Cracking the neural code for word recognition in convolutional neural networks.
PLoS computational biology
Sep 6, 2024
Learning to read places a strong challenge on the visual system. Years of expertise lead to a remarkable capacity to separate similar letters and encode their relative positions, thus distinguishing words such as FORM and FROM, invariantly over a lar...
Classification of Alzheimer disease using DenseNet-201 based on deep transfer learning technique.
PloS one
Sep 6, 2024
Alzheimer's disease (AD) is a brain illness that causes gradual memory loss. AD has no treatment and cannot be cured, so early detection is critical. Various AD diagnosis approaches are used in this regard, but Magnetic Resonance Imaging (MRI) provid...
Prediction of anhedonia in patients with first-episode schizophrenia using a Wavelet-ALFF-based Support vector regression model.
Neuroscience
Sep 5, 2024
Anhedonia is one of the core features of the negative symptoms of schizophrenia and can be extremely burdensome. Our study applied resting-state functional magnetic resonance imaging (fMRI)-based support vector regression (SVR) to predict anhedonia i...
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