Papers
5
Total Citations
16
H-Index
2
About
Ahona Ghosh is at the forefront of brain-computer interface (BCI) research, specializing in non-invasive EEG-based systems that empower individuals with motor and speech impairments. Her work centers on decoding neural signals—particularly motor imagery and upper limb movement—to control assistive robotics, such as wheelchairs and rehabilitative platforms. Ghosh’s major contributions include developing hybrid BCI paradigms that integrate multiple neural signals for more robust robot navigation, as demonstrated in her 2022 paper on automatic wheelchair control via EEG (7 citations). She has also pioneered data-driven approaches for cognitive rehabilitation, addressing speech disorders through hybrid sensor architectures. Her 2023 work on fuzzy vector quantization for motor imagery-induced wheelchair movement showcases her innovative use of machine learning to enhance system accuracy. With a growing citation impact and a focus on translating neural decoding into real-world assistive technologies, Ghosh’s research bridges the gap between human intent and robotic action, offering transformative solutions for rehabilitation and improved quality of life.
Research Focus
Key Achievements
Top Papers
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- 2Hybrid brain-computer interfacing paradigm for assistive robotics3 citations · 2024
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