Brain controlled robotic platform using steady state visual evoked potentials acquired by EEG

Dasgupta, Saumitro; Fanton, M.; Pham, Joshua; Willard, M.; Nezamfar, Hooman; Shafai, B.; Erdoğmuş, Deni̇z

doi:10.1109/acssc.2010.5757758

Cited by 11 publications

(1 citation statement)

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“…Datasets were then classified using Bayesian classifier for categorizing multi-class movement data. A motor-task study using SSVEP-based methods (Middendorf et al, 2000), employed feature extraction using Welch periodogram (512 FFT points) and involved SVM-based classification of turning right/going forward and stopping (Dasgupta et al, 2010). Neural Networks with backpropagation learning have been shown to classify arithmetic calculation task features, extracted using Burg method/Levison algorithm (auto-regression models) (Anderson, 1995).…”

Section: Neurological Mechanisms Used In Bcimentioning

confidence: 99%

Neural Control using EEG as a BCI Technique for Low Cost Prosthetic Arms

Diwakar

Bodda

Nutakki

et al. 2014

Proceedings of the International Conference on Neural Computation Theory and Applications

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There have been significant advancements in brain computer interface (BCI) techniques using EEG-like methods. EEG can serve as non-invasive BMI technique, to control devices like wheelchairs, cursors and robotic arm. In this paper, we discuss the use of EEG recordings to control low-cost robotic arms by extracting motor task patterns and indicate where such control algorithms may show promise towards the humanitarian challenge. Studies have shown robotic arm movement solutions using kinematics and machine learning methods. With iterative processes for trajectory making, EEG signals have been known to be used to control robotic arms. The paper also showcases a case-study developed towards this challenge in order to test such algorithmic approaches. Non-traditional approaches using neuro-inspired processing techniques without implicit kinematics have also shown potential applications. Use of EEG to resolve temporal information may, indeed, help understand movement coordination in robotic arm.

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Section: Neurological Mechanisms Used In Bcimentioning

confidence: 99%