EEG Correlation at a Distance: A Re-Analysis of Two Studies Using a Machine Learning Approach

16 Pages Posted: 14 Jan 2019

See all articles by Marco Bilucaglia

Marco Bilucaglia

EvanLab; IULM University

Luciano Pederzoli

University of Padova

William Giroldini

EvanLab

Elena Prati

EvanLab

Patrizio E. Tressoldi

Università di Padova

Date Written: December 16, 2018

Abstract

In this study, data from two studies relative to the relationship between the EEG activities of two isolated and physically separated subjects were re-analyzed using machine-learning algorithms. The first group of data consisted of 25 pairs of participants and the stimulation of one member of each pair consisted of continuous visual and auditory 500 Hz signals of 1 second duration, applied simultaneously. The other group of data consisted of 20 pairs of participants and one member of each pair received 900 Hz visual and auditory stimuli of 1 second duration with on-off modulation at 10, 12, and 14 Hz. When a ‘linear discriminant classifier’ was applied, it was possible in the first group of data to correctly classify 50.74% of the EEG activity of non-stimulated participants as correlated to the remote sensorial stimulation. In the second data group the percentage of correctly classified non-stimulated EEG activity was 51.17%, 50.45% e 51.91% respectively for the 10, 12, and 14 Hz stimulations. The analysis of EEG activity using machine-learning algorithms has produced advances in the study of the connection between the EEG activities of the stimulated partner and the isolated distant partner, proving that a relationship also exists regarding the stimulation frequency. Phenomenon will open new insight into the possibility of non-conventional connection of physically separated EEG signals.

Keywords: EEG, Correlation at Distance, Machine Learning, Linear Discrimination Analysis

Suggested Citation

Bilucaglia, Marco and Pederzoli, Luciano and Giroldini, William and Prati, Elena and Tressoldi, Patrizio E., EEG Correlation at a Distance: A Re-Analysis of Two Studies Using a Machine Learning Approach (December 16, 2018). Available at SSRN: https://ssrn.com/abstract=3303283 or http://dx.doi.org/10.2139/ssrn.3303283

Marco Bilucaglia

EvanLab ( email )

Firenze
Italy

IULM University ( email )

Milan
Italy

Luciano Pederzoli (Contact Author)

University of Padova ( email )

Via 8 Febbraio
Padova, 2-35122
Italy

William Giroldini

EvanLab ( email )

Firenze
Italy

Elena Prati

EvanLab ( email )

Firenze
Italy

Patrizio E. Tressoldi

Università di Padova ( email )

via Venezia 8
Padova, 35131
Italy

HOME PAGE: http://www.patriziotressoldi.it

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