RANCANG BANGUN ALAT DETEKSI DISLEKSIA BERBASIS ELEKTROENSEFALOGRAFI DAN MIKROKONTROLER

Dharmayuda Febrianto; Muhimmatul Khoiro

doi:10.26740/ifi.v15n1.p1-13

Authors

Dharmayuda Febrianto Universitas Negeri Surabaya
Muhimmatul Khoiro Universitas Negeri Surabaya

DOI:

https://doi.org/10.26740/ifi.v15n1.p1-13

Keywords:

Disleksia, EEG, Mikrokontroler, Gelombang Neuron, Bandpass Filter, Dyslexia

Abstract

Abstrak

Disleksia adalah gangguan neurobiologis yang memengaruhi kemampuan membaca, menulis, dan mengeja, yang berdampak pada sekitar 19,8% siswa sekolah dasar di Indonesia. Penelitian ini bertujuan untuk mengembangkan alat deteksi disleksia berbasis Elektroensefalografi (EEG) yang terintegrasi dengan mikrokontroler ESP32. Sistem ini dirancang untuk memproses sinyal EEG dari subjek normal dan disleksia untuk merekam data aktivitas listrik otak selama pengujian. Dalam penelitian ini, sistem deteksi terdiri dari elektroda EEG, penguat sinyal AD8232, dan mikrokontroler ESP32 yang mengirimkan data ke platform ThingSpeak untuk analisis lebih lanjut. Alat ini dikalibrasi menggunakan input Audio Function Generator (AFG) dan output osiloskop untuk memastikan kemampuan membaca sinyal frekuensi gelombang neuron (4 Hz, 20 Hz, 40 Hz). Proses pengambilan data dilakukan dengan menempatkan elektroda pada titik-titik tertentu di kepala subjek, diikuti dengan pencatatan aktivitas listrik otak saat subjek mengerjakan tugas tertentu. Data yang diperoleh kemudian diproses melalui filter bandpass untuk menyaring sinyal yang tidak relevan, sebelum diubah menjadi amplitudo frekuensi gelombang otak (theta, beta, gamma) yang digunakan untuk analisis. Hasil penelitian menunjukkan adanya perbedaan signifikan dalam amplitudo gelombang theta dan gamma antara subjek disleksia dan subjek normal, yang menandakan adanya gangguan dalam pemrosesan informasi kognitif pada subjek disleksia. Dengan demikian, sistem ini menawarkan solusi yang lebih efisien, portabel, dan terjangkau untuk deteksi dini disleksia. Alat ini berpotensi untuk memperluas aksesibilitas diagnosis dan intervensi yang lebih cepat dan tepat bagi anak-anak dengan disleksia, serta dapat menjadi dasar untuk pengembangan lebih lanjut dalam bidang ini.

Abstract

Dyslexia is a neurobiological disorder that affects the ability to read, write, and spell, impacting approximately 19.8% of primary school students in Indonesia. This study aims to develop a dyslexia detection tool based on Electroencephalography (EEG), integrated with the ESP32 microcontroller. The system is designed to process EEG signals from both normal and dyslexic subjects to record brain electrical activity during testing. In this study, the detection system consists of EEG electrodes, the AD8232 signal amplifier, and the ESP32 microcontroller, which sends data to the ThingSpeak platform for further analysis. The tool was calibrated using an Audio Function Generator (AFG) input and oscilloscope output to ensure the ability to read neural wave frequency signals (4 Hz, 20 Hz, 40 Hz). Data acquisition was performed by positioning electrodes at specific points on the subject's head, followed by recording brain activity as the subject performed a task. The obtained data was then processed through bandpass filtering to remove irrelevant signals, before being converted into amplitude frequency data for theta, beta, and gamma waves used for analysis. The results show a significant difference in the amplitude of theta and gamma waves between dyslexic and normal subjects, indicating disruptions in cognitive information processing in dyslexic subjects. Therefore, this system offers a more efficient, portable, and affordable solution for early dyslexia detection. The tool has the potential to expand accessibility to diagnosis and enable faster, more accurate interventions for children with dyslexia, and can serve as a foundation for further development in this field.

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