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This paper presents a novel approach to real-time decoding of visual and auditory brain signals using electroencephalography (EEG) data and advanced deep learning models. With the integration of transformer-based models for visual stimuli and a hybrid DALL-E/RNN architecture for auditory stimuli, we address current challenges in decoding accuracy and processing speed. The proposed method demonstrates improved signal fidelity and decoding efficiency, facilitating applications in neuro prosthetics, cognitive neuroscience, and assistive technology. Results indicate a significant performance enhancement over conventional GAN-based models. This work provides a foundational step toward efficient and scalable brain-computer interfaces capable of processing multi-modal brain signals in real time.
"Real-Time Visual and Auditory Brain Process Center Analysis Using EEG and Deep Learning", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.b120-b123, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504115.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator