Determining the Reading Comprehension Status of a Foreign Language Text Automatically with Functional Near Infrared Spectroscopy Signals and Sequential Forward Intrinsic Mode Function Selection Approach

Ural Akıncıoğlu; Önder Aydemir; Ahmet Çil; Muhammed Baydere

Makale detayları

Determining the Reading Comprehension Status of a Foreign Language Text Automatically with Functional Near Infrared Spectroscopy Signals and Sequential Forward Intrinsic Mode Function Selection Approach

Yazarlar:

Ural Akıncıoğlu | Önder Aydemir | Ahmet Çil | Muhammed Baydere

Yayıncı:

Giresun University

Ana Başlıklar:

Biyomedikal Bilimler ve Teknolojiler | Biyomedikal Mühendisliği (Diğer)

Dergi:

Karadeniz Fen Bilimleri Dergisi Vol.14 Issue 4, pp.2023-2035

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001333412

pISSN:

2564-7377

Anahtar kelimeler:

Özet:

 Nowadays, the level of knowledge of a foreign language is mostly determined by exams. In this study, neural activity signals recorded by functional near-infrared spectroscopy fNIRS while participants read a text in English as a foreign language are used to automatically determine the comprehension of the text. In the proposed method, linear interpolation and improved complementary ensemble empirical mode decomposition with adaptive noise ICEEMDAN were applied to the signals as preprocessing. With ICEEMDAN, intrinsic mode function IMF components were derived. In the feature extraction stage, the symbolic aggregate approximation method and the combination of kurtosis, skewness, and standard deviation statistical features were used. The extracted features were classified by the k-nearest neighbor k-NN classifier. The optimal IMF combination was determined by a sequential forward IMF selection approach using the training dataset. The proposed sequential forward IMF selection method-based approach was applied to 16 healthy participants and a classification accuracy of 85,37 was obtained with the k-NN Classifier on the test dataset. The results show that the comprehension of the whole English text can be classified as understooddid not understand with high accuracy through brain signals for which the most efficient IMF combination is determined.

 Nowadays, the level of knowledge of a foreign language is mostly determined by exams. In this study, neural activity signals recorded by functional near-infrared spectroscopy fNIRS while participants read a text in English as a foreign language are used to automatically determine the comprehension of the text. In the proposed method, linear interpolation and improved complementary ensemble empirical mode decomposition with adaptive noise ICEEMDAN were applied to the signals as preprocessing. With ICEEMDAN, intrinsic mode function IMF components were derived. In the feature extraction stage, the symbolic aggregate approximation method and the combination of kurtosis, skewness, and standard deviation statistical features were used. The extracted features were classified by the k-nearest neighbor k-NN classifier. The optimal IMF combination was determined by a sequential forward IMF selection approach using the training dataset. The proposed sequential forward IMF selection method-based approach was applied to 16 healthy participants and a classification accuracy of 85,37 was obtained with the k-NN Classifier on the test dataset. The results show that the comprehension of the whole English text can be classified as understooddid not understand with high accuracy through brain signals for which the most efficient IMF combination is determined.

 Nowadays, the level of knowledge of a foreign language is mostly determined by exams. In this study, neural activity signals recorded by functional near-infrared spectroscopy fNIRS while participants read a text in English as a foreign language are used to automatically determine the comprehension of the text. In the proposed method, linear interpolation and improved complementary ensemble empirical mode decomposition with adaptive noise ICEEMDAN were applied to the signals as preprocessing. With ICEEMDAN, intrinsic mode function IMF components were derived. In the feature extraction stage, the symbolic aggregate approximation method and the combination of kurtosis, skewness, and standard deviation statistical features were used. The extracted features were classified by the k-nearest neighbor k-NN classifier. The optimal IMF combination was determined by a sequential forward IMF selection approach using the training dataset. The proposed sequential forward IMF selection method-based approach was applied to 16 healthy participants and a classification accuracy of 85,37 was obtained with the k-NN Classifier on the test dataset. The results show that the comprehension of the whole English text can be classified as understooddid not understand with high accuracy through brain signals for which the most efficient IMF combination is determined.

 Günümüzde, insanların bir yabancı dili hangi seviyede bildikleri daha çok sınavlarla tespit edilmektedir. Bu çalışmada ise katılımcıların yabancı dil olarak İngilizce dilinde bir metni okurken fonksiyonel yakın kızılötesi spektroskopisi fYKS ile kaydedilen nöral aktivite sinyalleri kullanılarak ilgili metni anlayıp anlamadıkları otomatik olarak tespit edilmiştir. Önerilen metotta, ön işleme olarak doğrusal interpolasyon ve uyarlanabilir gürültüyle geliştirilmiş tamamlayıcı topluluk mod ayrıştırması ICEEMDAN sinyallere uygulanmıştır. ICEEMDAN ile sinyallerin içsel mod fonksiyon IMF bileşenleri türetilmiştir. Öznitelik çıkarma aşamasında ise sembolik toplam yaklaşım metodu ve basıklık, çarpıklık, standart sapma istatistiksel öznitelik kombinasyonu kullanılmıştır. Elde edilen öznitelikler, k-en yakın komşuluk k-NN sınıflandırıcısı ile sınıflandırılmıştır. Sınıflandırma sonucunda eğitim veri setinde ardışıl ileri IMF seçme yaklaşımı ile en uygun IMF kombinasyonu belirlenmiştir. Önerilen ardışıl ileri IMF seçim metodu esaslı yaklaşım 16 sağlıklı katılımcı üzerinde uygulanmış ve k-NN sınıflandırıcısı ile test verilerinde 85,37 sınıflandırma doğruluğu elde edilmiştir. Sonuçlar, İngilizce metnin tümünün anlaşılma durumunun en etkin IMF kombinasyonu belirlenen beyin sinyalleri aracılığıyla yüksek doğrulukla anladıanlamadı olarak sınıflandırılabileceğini göstermektedir.

Makale detayları

Determining the Reading Comprehension Status of a Foreign Language Text Automatically with Functional Near Infrared Spectroscopy Signals and Sequential Forward Intrinsic Mode Function Selection Approach

Yazarlar:

Ural Akıncıoğlu | Önder Aydemir | Ahmet Çil | Muhammed Baydere

Yayıncı:

Giresun University

Ana Başlıklar:

Biyomedikal Bilimler ve Teknolojiler | Biyomedikal Mühendisliği (Diğer)

Dergi:

Karadeniz Fen Bilimleri Dergisi Vol.14 Issue 4, pp.2023-2035

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001333412

pISSN:

2564-7377

Machine learning

fNIRS

Reading comprehension

Sequential forward IMF selectionMakine öğrenmesi

fYKS

Okuduğunu anlama

Ardışıl ileri yönlü IMF seçme

Sayı

Makale detayları

Yazarlar: Ural Akıncıoğlu | Önder Aydemir | Ahmet Çil | Muhammed Baydere

Yayıncı: Giresun University

Ana Başlıklar: Biyomedikal Bilimler ve Teknolojiler | Biyomedikal Mühendisliği (Diğer)

Dergi: Karadeniz Fen Bilimleri Dergisi Vol.14 Issue 4, pp.2023-2035

Belge Tipi: Makale

Yayın Tarihi: 12/2024

Belge Dili: Türkçe

Document ID (Org No.): 2001333412

pISSN: 2564-7377

Sayı

Yazarlar:

Ural Akıncıoğlu | Önder Aydemir | Ahmet Çil | Muhammed Baydere

Yayıncı:

Giresun University

Ana Başlıklar:

Biyomedikal Bilimler ve Teknolojiler | Biyomedikal Mühendisliği (Diğer)

Dergi:

Karadeniz Fen Bilimleri Dergisi Vol.14 Issue 4, pp.2023-2035

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001333412

pISSN:

2564-7377