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@article{189906,
        author = {Prajwal M P and Nithyashree S and Punith V and Kanmani B S},
        title = {EEG Based Prosthetic Arm - RoboGrip: A Grasp on Tomorrow},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {2620-2625},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189906},
        abstract = {A prosthetic arm is a meticulously crafted replacement for a missing upper extremity, designed to functionally replicate an individual's post-amputation dexterity and enhance their overall well-being. This paper explores the potential of using EEG signals to control prosthetic hands. While recent advancements have focused on EMG-based prosthetics, EEG offers a promising alternative. This study investigates the development of a BCI (Brain-Computer Interface) prosthetic arm using pre-recorded EEG data for six recognized grip patterns. The goal is to create a cost-effective, lightweight prosthetic capable of performing these essential functions. The paper details the process of utilizing machine learning algorithms to train the prosthetic model based on EEG data.},
        keywords = {BCI (Brain Computer Interface), EEG(Electroencephalogram), Prosthetic Arm, Grip Patterns, Algorithms.},
        month = {January},
        }