International Journal of Electrical, Electronics and Data Communication (IJEEDC)
eISSN:2320-2084 , pISSN:2321-2950
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Volume-7,Issue-12  ( Dec, 2019 )
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Statistics report
Feb. 2020
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 84
Paper Published : 1397
No. of Authors : 3784
  Journal Paper

Paper Title
Analysis of Electroencephalographic Signal Acquisition using EPOC Emotiv for use in Robotic Arm Moveentm

In recent years, new research has brought the field of electroencephalograph (EEG)-based brain–computer interfacing (BCI) out of its early stages into a phase of relative maturity through many demonstrated prototypes to assist the periphery compromised patients. It is a worth- while technology for disabled people enabling them to reinstate a damaged motor nerve or any neural pathway. This study introduces the development of Brain computer interface by means of a non-invasive wireless electroencephalograph (Emotiv EPOC sy ste m) and its application to control a prototy pe robotic arm. After the pre-processing and spectral analysis of raw EEG signals, an averaged peak value for selected channels were obtained as a feature vector for each movement. Two algorithms n a m e l y , linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used to differentiate the raw EEG data into their associative movements. Performance of a classification algorithms were assessed, which revealed that the accuracy was 71.77 (±0.76) % and 86.57(±0.79) % of LDA and QDA respectively. Feature vector resulted in superior performance of 86.57 (±0.79) % wi t h QDA. The averaged peak value of PSD for selected seven channels were then used to move a robotic arm successfully i n the two d i r e c t i o n s i . e . “Up (elbow flexion)” and “Down (elbow extension)”. Index Terms- Electroencephalogram (EEG), brain-controlled interface (BCI), Power spectrum d e n s i t y (PSD).

Author - Mashal Fatima, Muhammad Shafique

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| Published on 2019-04-12
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