Drone Movement Control by Electroencephalography Signals Based on BCI System
Yükleniyor...
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
VSB-TECHNICAL UNIV OSTRAVA, 17 LISTOPADU 15, OSTRAVA 70833, CZECH REPUBLIC
Erişim Hakkı
info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivs 3.0 United States
Attribution-NonCommercial-NoDerivs 3.0 United States
Özet
Brain Computer Interface enables individuals to communicate with devices through ElectroEncephaloGraphy (EEG) signals in many applications that use brainwave-controlled units. This paper presents a new algorithm using EEG waves for controlling the movements of a drone by eye-blinking and attention level signals. Optimization of the signal recognition obtained is carried out by classifying the eyeblinking with a Support Vector Machine algorithm and converting it into 4-bit codes via an artificial neural network. Linear Regression Method is used to categorize the attention to either low or high level with a dynamic threshold, yielding a 1-bit code. The control of the motions in the algorithm is structured with two control layers. The first layer provides control with eye-blink signals, the second layer with both eye-blink and sensed attention levels. EEG signals are extracted and processed using a single channel NeuroSky MindWave 2 device. The proposed algorithm has been validated by experimental testing of five individuals of different ages. The results show its high performance compared to existing algorithms with an accuracy of 91.85 % for 9 control commands. With a capability of up to 16 commands and its high accuracy, the algorithm can be suitable for many applications.
Açıklama
Anahtar Kelimeler
Attention level, Brain Computer Interface (BCI), ElectroEncephaloGraphy (EEG), eye-blink, NeuroSky MindWave 2
Kaynak
Advances in Electrical and Electronic Engineering
WoS Q Değeri
N/A
Scopus Q Değeri
Q4
Cilt
20
Sayı
2
