Ballı, Tuğçe

Profile Picture
Name Variants
T. Ballı
Ballı, TUĞÇE
Altuğlu T.
Altuǧlu T.
Ballı, T.
Balli, Tugce
B., Tugce
Tugce, Balli
TUĞÇE BALLI
Tuğçe Ballı
BALLI, Tuğçe
B., Tuğçe
Ballı T.
Ballı, Tuğçe
Ballı,T.
Balli T.
Balli,Tugce
Balli,T.
B.,Tugce
Tuğçe BALLI
BALLI, TUĞÇE
Job Title
Dr. Öğr. Üyesi
Email Address
tugce.balli@khas.edu.tr
ORCID ID
0000-0002-6509-3725
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Scholarly Output

1

Articles

1

Citation Count

0

Supervised Theses

0

Filters

20231
Reset filters

Settings

Scholarly Output Search Results

Now showing 1 - 1 of 1
  • Thumbnail Image
    Article
    Citation Count: 0
    Comparative classification performances of filter model feature selection algorithms in EEG based brain computer interface system
    (Gazi Univ, Fac Engineering Architecture, 2023) Ballı, Tuğçe; Balli, Tugce; Yetkin, E. Fatih
    Brain-computer interface (BCI) systems enable individuals to use a computer or assistive technologies such as a neuroprosthetic arm by translating their brain electrical activity into control commands. In this study, the use of filter-based feature selection methods for design of BCI systems is investigated. EEG recordings obtained from a BCI system designed for the control of a neuroprosthetic device are analyzed. Two feature sets were created; the first set was band power features from six main frequency bands (delta (1.0-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-25 Hz), high-beta (25-30Hz) and gamma (30-50 Hz)) and the second set was band power features from ten frequency sub-bands (delta (1-4 Hz), theta (4-8 Hz), alpha1 (8-10 Hz), alpha2 (10-12 Hz), beta1 (12-15 Hz), beta2 (15-18 Hz), beta3 (18-25 Hz), gamma1 (30-35 Hz), gamma2 (35-40 Hz), gamma3 (40-50 Hz)). Ten filter-based feature selection methods are investigated along with linear discriminant analysis, random forests, decision tree and support vector machines algorithms. The results indicate that feature selection methods leads to a higher classification accuracy and eigen value centrality (Ecfs) and infinite feature selection (Inffs) methods have consistently provided higher accuracy rates as compared to rest of the feature selection methods.