• Suyash Sandip Vetal
• Sanket Pravin Bartakke
• Chaitanya Rajendra Sarowar
• Tejas Dattatray Tekawade
• Mayura Shelke

Electroencephalography (EEG) data analysis plays a crucial role in understanding brain function and diagnosing neurological disorders. However, traditional methods often struggle with the non-linear and dynamic nature of EEG signals. Our work introduces Liquid Time Constant (LTC) networks, a novel deep learning architecture specifically designed for time-series analysis, to the field of EEG data analysis. LTC networks offer several advantages over conventional methods. The uniqueness of the liquid time constant mechanism enables them to adaptively capture temporal dependencies in the data, leading to superior performance in tasks like signal classification and prediction. Additionally, their inherent stability and bounded behaviour make them well-suited for real-time applications.

Liquid Time-Constant networks, Deep Learning. Deep recurrent neural networks (DRNNs), Ordinary differential equations (ODEs), Computational neuroscience, Brain-computer interfaces (BCI), EEG data analysis, Time-series analysis.

"INTRODUCING CONTINUOUS-TIME NETWORKS TO THE ELECTROENCEPHALOGRAPHY (EEG) ANALYSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.h101-h106, May-2024, Available :http://www.jetir.org/papers/JETIR2405712.pdf

"INTRODUCING CONTINUOUS-TIME NETWORKS TO THE ELECTROENCEPHALOGRAPHY (EEG) ANALYSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. pph101-h106, May-2024, Available at : http://www.jetir.org/papers/JETIR2405712.pdf