Implementation of EEG signals and P-300 Component to estimate Mild Cognitive Impairment (MCI)
Parag Puranik1, Santosh Agrahari2, Ashish Panat3 

1Parag Puranik, Department of Engineering, and Communication Engineering, School of Engineering & Technology, Poornima University, Jaipur, India.
2Dr. Santosh Agrahari, Department of Engineering, and Communication Engineering, School of Engineering& Technology, Poornima University, Jaipur, India.
3Dr. Ashish Panat, Department of Electronics and Communication, MIT University, Pune, India.

Manuscript received on 19 March 2019 | Revised Manuscript received on 26 March 2019 | Manuscript published on 30 July 2019 | PP: 4381-4391 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3188078219/19©BEIESP | DOI: 10.35940/ijrte.B3188.078219
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Abstract: The aim is to estimate the parameters of MCI by evaluating the EEG and P-300 component of subjects. The controlled healthy and MCI patients selected for this analysis. The aim is to reveal the worsening cognition in the patients and diagnose the disease at an early stage. Method: EEG recording & P-300 measurement of 30 subjects is performed. Considering all the possibilities and artefacts 1024-point Quantitative EEG selected to perform the analysis. Results: The parameters of EEG and P-300 analysis revealed the difference between Controlled healthy and MCI group patients. Power, relative power, symmetry, coherence, phase cross spectrum, correlation were differentiated using QEEG analysis. Conclusion: The study on MCI patients discovered that the mass posterior sluggish rhythm of frequency bands dropped the alpha and beta behavior whereas the occipital movement of the alpha and beta band in the usual aging is increasing. The P-300 component used to classify MCI and Controlled healthy people.
Index Terms: EEG, MCI (Mild Cognitive Impairment), Power Spectrum, P-300, Statistical Analysis

Scope of the Article: Component-Based Software Engineering