Výsledky vyhledávání - "Siddhad, Gourav"

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DrowzEE-G-Mamba: Leveraging EEG and State Space Models for Driver Drowsiness Detection

Autor: Siddhad, Gourav, Dey, Sayantan, Roy, Partha Pratim

Driver drowsiness is identified as a critical factor in road accidents, necessitating robust detection systems to enhance road safety. This study proposes a driver drowsiness detection system, DrowzEE-G-Mamba, that combines Electroencephalography (EE

Externí odkaz: http://arxiv.org/abs/2408.16145

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Neural Networks Meet Neural Activity: Utilizing EEG for Mental Workload Estimation

Autor: Siddhad, Gourav, Roy, Partha Pratim, Kim, Byung-Gyu

Electroencephalography (EEG) offers non-invasive, real-time mental workload assessment, which is crucial in high-stakes domains like aviation and medicine and for advancing brain-computer interface (BCI) technologies. This study introduces a customiz

Externí odkaz: http://arxiv.org/abs/2408.13930

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Awake at the Wheel: Enhancing Automotive Safety through EEG-Based Fatigue Detection

Autor: Siddhad, Gourav, Dey, Sayantan, Roy, Partha Pratim, Iwamura, Masakazu

Driver fatigue detection is increasingly recognized as critical for enhancing road safety. This study introduces a method for detecting driver fatigue using the SEED-VIG dataset, a well-established benchmark in EEG-based vigilance analysis. By employ

Externí odkaz: http://arxiv.org/abs/2408.13929

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Enhancing EEG Signal-Based Emotion Recognition with Synthetic Data: Diffusion Model Approach

Autor: Siddhad, Gourav, Iwamura, Masakazu, Roy, Partha Pratim

Emotions are crucial in human life, influencing perceptions, relationships, behaviour, and choices. Emotion recognition using Electroencephalography (EEG) in the Brain-Computer Interface (BCI) domain presents significant challenges, particularly the

Externí odkaz: http://arxiv.org/abs/2401.16878

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Efficacy of Transformer Networks for Classification of Raw EEG Data

Autor: Siddhad, Gourav, Gupta, Anmol, Dogra, Debi Prosad, Roy, Partha Pratim

Publikováno v: Biomedical Signal Processing and Control, Vol 87, 2023

With the unprecedented success of transformer networks in natural language processing (NLP), recently, they have been successfully adapted to areas like computer vision, generative adversarial networks (GAN), and reinforcement learning. Classifying e

Externí odkaz: http://arxiv.org/abs/2202.05170

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