Observed shape detection from EEG time series
[ X ]
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
ULAKBİM
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Brain computer interface studies required
recording of a physiological response of a subject to exhibit
relevant information. This extracted information can be
used to perform an action and the amount of the information
plays a significant role in the determination of brain
computer interface (BCI) performance. The use of improved
experimental paradigms as well as measuring the brain
responses using electroencephalogram (EEG) is the most
common approach for the BCI studies. In this study, the
classification of the ongoing brain activity occurring as
response to the four shapes is managed and reported. We
applied Fourier transform to obtain the frequency spectrum
regarding the one second time series of each channel with a
time overlap of 50% to the feature set of each stimulus type.
Four machine learning classifiers are implemented, and in
the concept of the classification, (delta, theta, alpha, beta,
and gamma) band power values for one second period
constituted the feature set, resulting in a total of 315 features.
Among the four ML classifier Quadratic Discriminant
87.1% recorded the highest accuracy.
Açıklama
Anahtar Kelimeler
Electroencephalogram, Shape Detection, Classification, Machine Learning
Kaynak
IEEE
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Alobaidi, M., Duru, A. D., & Bayat, O. (2021, November). Observed Shape Detection from EEG Time Series. In 2021 IEEE 19th Student Conference on Research and Development (SCOReD) (pp. 278-283). IEEE.
