EEG Sinyalleri Kullanılarak Makine Öğrenmesi Tabanlı Otomatik Duygu Sınıflandırma

Year 2023, , 26 - 40, 30.06.2023

Hakan Köksal , Mehmet Bayğın

https://doi.org/10.5281/zenodo.8074861

Abstract

Son yıllarda otomatik duygu tespiti ve sınıflandırma literatürde üzerinde çalışılan konular arasında yer almaktadır. Duygular, bireylerin dış dünyayla olan ilişkilerinde, eylemlerinde ve kararlarında etkin rol oynamaktadır. Bu nedenle duygu tanıma insan-bilgisayar etkileşimde kritik öneme sahiptir. Duyguların tespiti yapılırken EEG sinyallerinin bazı nörolojik ve beyinsel aktiviteleri tespit ettiği tartışma konusudur. Bu çalışmada, duyguların tespiti ve analizi için ayırıcı özellik taşıyan sinyaller üretildiğinden EEG sinyalleri kullanılmıştır. Bu çalışmada etkili ve basit yöntemler geliştirerek yüksek doğruluğa sahip otomatik duygu tanıma amaçlanmaktadır. Önerilen Local Binary Pattern (LBP) yönteminde ReliefF özellik seçimi ve Ensemble sınıflandırıcı kullanılarak yeni bir otomatik EEG duygu tanıma modeli sunulmuştur. Bu model, özellik çıkarma, özellik seçme ve sınıflandırma olmak üzere makine öğrenimi modelinin tüm aşamalarını kapsamaktadır. Bu model üzerinde yapılan çalışmada, DREAMER veri seti üzerinden ortalama %63.89 sınıflandırma başarısına ulaşılmıştır.

Keywords

EEG, Makine Öğrenmesi, Duygu Tanıma, Sınıflandırma

Machine Learning Based Automatic Emotion Classification Using EEG Signals

Abstract

In recent years, automatic emotion detection and classification are among the topics studied in the literature. Emotions play an active role in individuals' relations with the outside world, their actions and decisions. Therefore, emotion recognition is critical in human-computer interaction. It is a matter of debate that EEG signals detect some neurological and cerebral activities while detecting emotions. In this study, EEG signals were used as distinctive signals were produced for the detection and analysis of emotions. In this study, automatic emotion recognition with high accuracy is aimed by developing effective and simple methods. In the proposed Local Binary Pattern (LBP) method, a new automatic EEG emotion recognition model is presented using ReliefF feature selection and Ensemble classifier. This model covers all phases of the machine learning model, including feature extraction, feature selection, and classification. In the study conducted on this model, an average of 63.89% classification success was achieved over the DREAMER data set.

Keywords

EEG, Machine Learning, Emotion Recognition, Classification

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