Results 1 to 10 of about 829,836 (72)

Differential quadruple pattern: A new EEG signal classification framework [PDF]

IBRO Neuroscience Reports
EEG signals are the letters of the brain and reflect neural activity. Abnormal EEG patterns indicate brain disorders such as epilepsy. Recently, machine learning has enabled automated EEG interpretation with high accuracy.
Bilge Ozgor, Omer Faruk Goktas, Mehmet Baygin, Sengul Dogan, Turker Tuncer   +4 more
doaj   +2 more sources

Different pattern: a new EEG-based method for mental performance detection [PDF]

BMC Medical Informatics and Decision Making
Background This study focuses on detecting mental performance from EEG signals. It provides both classification and explanation results. For this purpose, we developed a new feature extraction method called Different Pattern (DiffPat) within an ...
Ugur Ince, Serkan Kirik, Irem Tasci, Prabal Datta Barua, Mehmet Baygin, Burak Tasci, Sengul Dogan, Turker Tuncer   +7 more
doaj   +2 more sources

Quantum inspired feature engineering for explainable EEG signal classification [PDF]

Scientific Reports
In this research, our main objective is to extract more informative features by deploying a simple and effective framework. One of the cheapest data-gathering methods from the brain is electroencephalography signal collection.
Fahad A. Alotaibi, Mehmet Said Nur Yagmahan, Khalid A. Alobaid, Mousa Jari, Omer Faruk Goktas, Mehmet Baygin, Sengul Dogan, Turker Tuncer   +7 more
doaj   +2 more sources

CubicPat: Investigations on the Mental Performance and Stress Detection Using EEG Signals [PDF]

Diagnostics
Background\Objectives: Solving the secrets of the brain is a significant challenge for researchers. This work aims to contribute to this area by presenting a new explainable feature engineering (XFE) architecture designed to obtain explainable results ...
Ugur Ince, Yunus Talu, Aleyna Duz, Suat Tas, Dahiru Tanko, Irem Tasci, Sengul Dogan, Abdul Hafeez Baig, Emrah Aydemir, Turker Tuncer   +9 more
doaj   +2 more sources

DiagPat: An Explainable Language Detection Model Using EEG Signals [PDF]

Sensors
Electroencephalography (EEG) offers a non-invasive and cost-effective means of probing brain activity during language processing; however, prior EEG-based language studies have been limited by small datasets, a predominant focus on native-speaker or ...
Tugce Keles, Kubra Yildirim, Dahiru Tanko, Suat Tas, Irem Tasci, Burak Tasci, Gulay Tasci, Turker Tuncer, Sengul Dogan   +8 more
doaj   +2 more sources

ChMinMaxPat: Investigations on Violence and Stress Detection Using EEG Signals [PDF]

Diagnostics
Background and Objectives: Electroencephalography (EEG) signals, often termed the letters of the brain, are one of the most cost-effective methods for gathering valuable information about brain activity.
Omer Bektas, Serkan Kirik, Irem Tasci, Rena Hajiyeva, Emrah Aydemir, Sengul Dogan, Turker Tuncer   +6 more
doaj   +2 more sources

Novel accurate classification system developed using order transition pattern feature engineering technique with physiological signals [PDF]

Scientific Reports
This paper presents a novel, explainable feature engineering framework for classifying EEG and ECG signals with high accuracy. The proposed method employs the Order Transition Pattern (OTPat) feature extractor.
Mehmet Ali Gelen, Prabal Datta Barua, Irem Tasci, Gulay Tasci, Emrah Aydemir, Sengul Dogan, Turker Tuncer, U. R. Acharya   +7 more
doaj   +2 more sources

TensorCSBP: A Tensor Center-Symmetric Feature Extractor for EEG Odor Detection [PDF]

Diagnostics
Objective: Accurate odor classification from EEG signals requires informative and interpretable features. Although Local Binary Pattern (LBP) and variants such as the center-symmetric binary pattern are widely used, they lack sufficient explainability ...
Irem Tasci, Ilknur Sercek, Yunus Talu, Prabal Datta Barua, Mehmet Baygin, Burak Tasci, Sengul Dogan, Turker Tuncer   +7 more
doaj   +2 more sources

TATPat based explainable EEG model for neonatal seizure detection [PDF]

Scientific Reports
The most cost-effective data collection method is electroencephalography (EEG) to obtain meaningful information about the brain. Therefore, EEG signal processing is very important for neuroscience and machine learning (ML).
Turker Tuncer, Sengul Dogan, Irem Tasci, Burak Tasci, Rena Hajiyeva   +4 more
doaj   +2 more sources

QuadTPat: Quadruple Transition Pattern-based explainable feature engineering model for stress detection using EEG signals [PDF]

Scientific Reports
The most cost-effective data collection method is electroencephalography (EEG), which obtains meaningful information about the brain. Therefore, EEG signal processing is crucial for neuroscience and machine learning (ML).
Veysel Yusuf Cambay, Irem Tasci, Gulay Tasci, Rena Hajiyeva, Sengul Dogan, Turker Tuncer   +5 more
doaj   +2 more sources