Research Database
Curated neuroscience and AI research papers
Showing 20 of 100 papers
Deep learning for electroencephalogram (EEG) classification tasks: a review.
Craik, Alexander; He, Yongtian; Contreras-Vidal, Jose L
Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain-computer interfaces, BCI's), and even commercial...
Machine learning applications on neuroimaging for diagnosis and prognosis of epilepsy: A review.
Yuan, Jie; Ran, Xuming; Liu, Keyin; Yao, Chen; Yao, Yi et al.
Machine learning is playing an increasingly important role in medical image analysis, spawning new advances in the clinical application of neuroimaging. There have been some reviews on machine...
Deep social neuroscience: the promise and peril of using artificial neural networks to study the social brain.
Sievers, Beau; Thornton, Mark A
This review offers an accessible primer to social neuroscientists interested in neural networks. It begins by providing an overview of key concepts in deep learning. It then discusses three ways...
Natural and Artificial Intelligence: A brief introduction to the interplay between AI and neuroscience research.
Macpherson, Tom; Churchland, Anne; Sejnowski, Terry; DiCarlo, James; Kamitani, Yukiyasu et al.
Neuroscience and artificial intelligence (AI) share a long history of collaboration. Advances in neuroscience, alongside huge leaps in computer processing power over the last few decades, have given...
Neural network models and deep learning.
Kriegeskorte, Nikolaus; Golan, Tal
Originally inspired by neurobiology, deep neural network models have become a powerful tool of machine learning and artificial intelligence. They can approximate functions and dynamics by learning...
Perturbing BEAMs: EEG adversarial attack to deep learning models for epilepsy diagnosing.
Yu, Jianfeng; Qiu, Kai; Wang, Pengju; Su, Caixia; Fan, Yufeng et al.
Deep learning models have been widely used in electroencephalogram (EEG) analysis and obtained excellent performance. But the adversarial attack and defense for them should be thoroughly studied...
Deep learning-based EEG analysis: investigating P3 ERP components.
Borra, Davide; Magosso, Elisa
The neural processing of incoming stimuli can be analysed from the electroencephalogram (EEG) through event-related potentials (ERPs). The P3 component is largely investigated as it represents an...
Deep Learning Recognition of Paroxysmal Kinesigenic Dyskinesia Based on EEG Functional Connectivity.
Zhao, Liang; Zou, Renling; Jin, Linpeng
Paroxysmal kinesigenic dyskinesia (PKD) is a rare neurological disorder marked by transient involuntary movements triggered by sudden actions. Current diagnostic approaches, including genetic...
Lightweight deep learning models for EEG decoding: a review.
Li, Yizhen; Chen, Enze; Xiao, Xiaolin; Xu, Minpeng; Ming, Dong
Brain-computer interface (BCI) technology enables direct communication between the human brain and external devices by decoding electroencephalography (EEG)signals into actionable commands. As a...
Emotion recognition in EEG signals using deep learning methods: A review.
Jafari, Mahboobeh; Shoeibi, Afshin; Khodatars, Marjane; Bagherzadeh, Sara; Shalbaf, Ahmad et al.
Emotions are a critical aspect of daily life and serve a crucial role in human decision-making, planning, reasoning, and other mental states. As a result, they are considered a significant factor in...
Deep learning for automated epileptiform discharge detection from scalp EEG: A systematic review.
Nhu, Duong; Janmohamed, Mubeen; Antonic-Baker, Ana; Perucca, Piero; O'Brien, Terence J et al.
Automated interictal epileptiform discharge (IED) detection has been widely studied, with machine learning methods at the forefront in recent years. As computational resources become more accessible,...
An empirical comparison of deep learning explainability approaches for EEG using simulated ground truth.
Sujatha Ravindran, Akshay; Contreras-Vidal, Jose
Recent advancements in machine learning and deep learning (DL) based neural decoders have significantly improved decoding capabilities using scalp electroencephalography (EEG). However, the...
An ensemble deep-learning approach for single-trial EEG classification of vibration intensity.
Alsuradi, Haneen; Park, Wanjoo; Eid, Mohamad
. Single-trial electroencephalography (EEG) classification is a promising approach to evaluate the cognitive experience associated with haptic feedback. Convolutional neural networks (CNNs), which...
Overview of functional magnetic resonance imaging.
Glover, Gary H
Blood Oxygen Level Dependent (BOLD) functional magnetic resonance imaging (fMRI) depicts changes in deoxyhemoglobin concentration consequent to task-induced or spontaneous modulation of neural...
What we can do and what we cannot do with fMRI.
Logothetis, Nikos K
Functional magnetic resonance imaging (fMRI) is currently the mainstay of neuroimaging in cognitive neuroscience. Advances in scanner technology, image acquisition protocols, experimental design, and...
Brain-Computer Interface: Applications to Speech Decoding and Synthesis to Augment Communication.
Luo, Shiyu; Rabbani, Qinwan; Crone, Nathan E
Damage or degeneration of motor pathways necessary for speech and other movements, as in brainstem strokes or amyotrophic lateral sclerosis (ALS), can interfere with efficient communication without...
Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects.
Ahmed, Md Rishad; Zhang, Yuan; Feng, Zhiquan; Lo, Benny; Inan, Omer T et al.
Dementia, a chronic and progressive cognitive declination of brain function caused by disease or impairment, is becoming more prevalent due to the aging population. A major challenge in dementia is...
Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review.
Buchlak, Quinlan D; Esmaili, Nazanin; Leveque, Jean-Christophe; Bennett, Christine; Farrokhi, Farrokh et al.
Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for detecting,...
Human Brain Inspired Artificial Intelligence Neural Networks.
Theotokis, Paschalis
It is becoming increasingly evident that Artificial intelligence (AI) development draws inspiration from the architecture and functions of the human brain. This manuscript examines the alignment...
The application of cognitive neuroscience to judicial models: recent progress and trends.
Zhang, Ni; Zhang, Zixuan
Legal prediction presents one of the most significant challenges when applying artificial intelligence (AI) to the legal field. The legal system is a complex adaptive system characterized by the...