Category: DSP

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DSP Neurosains

EEG-Based Emotion Classification Using Wavelet Decomposition and K-Nearest Neighbor

Agfianto Eko Putra, Catur Atmaji, and Fajrul Ghaleb

In the area of affective computing technology, the classification of emotions can be used for a variety of things such as health, entertainment, education, etc. This study determined the classification of emotions based on EEG (Electroencephalography) signals, which is emotions are classified according to the 2-dimensional graphics modeling of arousal and valence. This research uses a wavelet decomposition method to get features from the EEG signal. Features taken from the signal is a power signal decomposition of sub-band theta, alpha, beta, and gamma. These features are derived from the 5 levels decomposition using Coiflet2 and Daubechies2 mother wavelet. Classification is done using k-Nearest Neighbor (kNN) with the closest neighbor calculated based on correlation distance. Data validation is done using 5-folds cross-validation for validation of test data and training data. The highest accuracy obtained by using the mother wavelet Coiflet 2 with kNN parameter k=21. Valence classification accuracy is 57.5%, and accuracy of arousal is 63.98%.

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DSP

Three-Class Classification of EEG Signals Using Support Vector Machine Methods

Catur Atmaji, Agfianto Eko Putra, and Irvan Albab Tontowi

Many research on how the human brain works have been done in the last century. The use of electroencephalogram signal generated from quantifying the brainwave has been developed in many areas including the development of brain-computer interface (BCI) concept. One type of BCI that interesting for the future use is motor imagery (MI) based-BCI which only requiring imagination of a person to control an object. This study proposed a feature extraction in eight different channels using discrete wavelet (DWT) coefficients. The wavelet coefficient is transformed to a frequency domain using discrete Fourier transform (DFT) and then average power spectrum is calculated. Level 5 of detail component of the DWT is chosen because, from 512Hz sampling frequency (8 – 16Hz), it resembles mu rhythm of brain wave (8 – 12Hz) which affected from motor imagery activity. The classification of three classes, which are the imagination of right body movement, left the movement, and random word using multiclass support vector machine (SVM) shows a promising result with a sensitivity of 96.88%, 86.12% and 52.78% from three different subjects.

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DSP

Detecting proper nouns in indonesian-language translation of the quran using a guided method

Suwanto Raharjo, Retantyo Wardoyo, Agfianto Eko Putra

Proper nouns (often abbreviated PN or NNP) are a class of words important in labeling and subsequent text processing, especially in natural language processing (NLP). Name entity recognition (NER) is one study that requires PN. The lack of labeled data for Indonesian text, especially the PN label, may be attributed for the lack of NER research in the Indonesian language. This study aims to detect PN in Indonesian-language translations of the Quran guided by deriving location information from the Quran as its source text. In the Indonesian language, PN is written using initial capital letters, which are used to determine and guide PN location. This article proposes that PN in Indonesian-language translations of the Quran can be determined based on PoS Information of Quranic text by developing a certain production rule. The results of this research showed that the proposed method has promising results for further research.

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DSP

Sliding Window Method for Eye Movement Detection based on Electrooculogram Signal

By Catur Atmaji, Agfianto Eko Putra, Hanif Arrijal

Abstract

In the past few decades, biomedical signals have played important roles in assisting diagnosis for medical purposes. After the rose of brain-computer interfaces (BCI) and human-machine interaction (HMI) concept, biomedical signals such as an electroencephalograph (EEG) and electrooculogram (EOG) begun to be implemented in control and communication systems. EOG, the signal resulted from eye movement, has been used to design various applications from drowsiness detection to virtual keyboard control. The key of the system developed from EOG signal is the detection system for every eye movement. In this study, a sliding window technique is proposed to make eye movement patterns easier be formulated and using overlap window to avoid local extrema when computing the feature. Evaluation of this method shows that combination of 0.5 s-window length and 25% overlap give 17% and 1% false discovery rate (FDR) in the vertical and horizontal channel while the true positive rate (TPR) in both channels is 98%. The combination of automatic window and 25% overlap give a better accuracy with 99% and 100% TPR in the two directions while the FDRs are 22% and 1%.

Please contact Mr. Catur Atmaji for more information.

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DSP Neurosains

The Application of Music Therapy For Children with Autism Based On Facial Recognition Using Eigenface Method

by Hesti Khuzaimah Nurul Yusufiyah, Ilona Usuman, Agfianto Eko Putra, Triyogatama Wahyu Widodo

This research is motivated by the condition of children with autism who require continuous monitoring without a parent accompany, and certain therapies who is capable to reduce repetitive behaviors and increase the concentration. Music therapy is one of the treatment which able to perform it. The implementation of this research is located in a special schools with autism, the perform is proper at certain times and requires tools for music therapy. Therefore, the integrated device was made to monitor the activities of children with autism, as well as providing music therapy automatic face recognition based on Eigenface method.This device perform when the children with autism under certain conditions (e.g. crying, the teacher would not work orders, etc.), then by capturing the the facial image, the system will process to compare the similarity with the existing database. Then, the shortest distance of euclidean is chosen. If the captured of facial image is similar to the one existing database, then the music is performed as music therapy for children with autism. The results of this system, indicates that the child’s responses become more calm, easy concentration, and the repetitive attitude is reduced. While the accuracy of the system achieves by 80% (compare with the old and new database) and 20%. (without new database).

©2017 JNSMR UIN Walisongo. All rights reserved. [click here for more information]

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DSP

Rancang Bangun Spectrum Analyzer Menggunakan Fast Fouier Transform Pada Single Board Computer

Oleh Afandi Nur Aziz Thohari, Agfianto Eko Putra

Spectrum analyzer merupakan alat yang berfungsi untuk mengubah sinyal dalam ranah waktu menjadi spektrum dalam ranah frekuensi. Sebuah alat penganalisa sinyal umumnya memiliki ukuran yang besar sebab terdiri dari banyak komponen seperti mixer, amplifier, local osilator, ADC dll. Selain ukuran, masalah dari spectrum analyzer yang ada di pasaran adalah tingginya utilitas yang disebabkan oleh banyaknya titik cuplikan dari sinyal. Utilitas yang tinggi mengakibatkan spektrum yang ditampilkan menjadi lambat. Oleh sebab itu, dalam penelitian ini dirancang purwarupa spectrum analyzer berukuran kecil menggunakan single board computer, RTL-SDR dan LCD touchscreen. Fungsi spectrum analyzer diimplementasikan dalam sebuah perangkat lunak dengan menerapkan algoritma fast fourier transform. Masukan yang diproses berupa sinyal radio yang dicuplik dalam beberapa titik untuk mengetahui utilitas dari SBC. Kesimpulan yang diperoleh dari hasil pengujian yaitu utilitas SBC dapat menampilkan spektrum dengan normal pada jumlah cuplikan (N) dari 512 sampai 32.768 titik. Sebab penggunaan N lebih dari 32.768 titik akan membebani cpu dan memori sehingga spektrum yang ditampilkan menjadi lambat. Kemudian jangkauan frekuensi yang dapat ditampilkan purwarupa oleh adalah 24 MHz sampai 1.769 MHz. Purwarupa dapat menunjukan letak spektrum radio secara tepat setelah dilakukan perbandingan level spektrum menggunakan spectrum analyzer Anritsu MS2720T.

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DSP

Identification of Voice Utterance with Aging Factor Using the Method of MFCC Multichannel

This research was conducted to develop a method to identify voice utterance. For voice utterance that encounters change caused by aging factor, with the interval of 10 to 25 years. The change of voice utterance influenced by aging factor might be extracted by MFCC (Mel Frequency Cepstrum Coefficient). However, the level of the compatibility of the feature may be dropped down to 55%. While the ones which do not encounter it may reach 95%. To improve the compatibility of the changing voice feature influenced by aging factor, then the method of the more specific feature extraction is developed: which is by separating the voice into several channels, suggested as MFCC multichannel, consisting of multichannel 5 filterbank (M5FB), multichannel 2 filterbank (M2FB) and multichannel 1 filterbank (M1FB). The result of the test shows that for model M5FB and M2FB have the highest score in the level of compatibility with 85% and 82% with 25 years interval. While model M5FB gets the highest score of 86% for 10 years time interval.

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DSP Mikrokontroler Neurosains

EEG-Based Microsleep Detector using Microcontroller

Tifani Galuh Utami, Agfianto Eko Putra and Catur Atmaji

Drowsiness has symptoms which are itchy eyes, slow eye blink movement, smaller pupils, yawning and even a body. But the driver ignores it when the body send one of those signals often. The impacts which can occur to the driver, such as make a wrong decision while driving, could happen and lead to the most car accident reason. Therefore, the system which can provide an alarm when the driver feels drowsiness, fatigue or even microsleep is required. The way to detect microsleep when it occurs is to use the Electroencephalograph (EEG) brainwave. The system uses the one channel EEG Sensor device developed by Neurosky Mindwave which can provide eight brainwave signal such as Delta, Theta, Low Alpha, High Alpha, Low Beta, High Beta, Low Gamma, and Mid Gamma. On the other hand, attention and relaxation value can be generated as well. This prototype system tested by the car driver achieved its purpose of detecting microsleep event and alerting the driver by the alarm.

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DSP

Rangkuman artikel Pemrosesan Sinyal Digital sepanjang 2008-2015

Sungguh menyenangkan bisa menulis dan berbagi berbagai macam artikel atau paper yang berkaitan dengan Pemrosesan Sinyal Digital atau PSD yang sudah sekian tahun saya geluti, dan sebagaimana saya telah membuat rangkuman artikel tentang mikrokontroler dan PLC, berikut ini adalah rangkuman tentang artikel/paper tentang PSD atau DSP yang telah saya tulis sepanjang tahun 2008 – 2015, selamat membaca dan berdiskusi…

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DSP

Grid-based Histogram of Oriented Optical Flow for analyzing movements on video data

Detection and recognition of object movements in a video is one of the research topics that are popular today. For the purposes of the analysis of the object movements in the video, the direction of movement is the important feature. In this study, we proposed a new method for determining the direction of movement using Histogram of Oriented Optical Flow (HOOF). We extract it locally at every N-by-N grid, not the entire frame. Direction movement is determined based on the value of HOOF on every grid. We classify the direction of movement in each grid into 12 directions. We use a video from UMN datasets for testing the proposed method. The experiment results show the value of False Positive Per Grid (FPPG) is 28.32%, and False Negative Per Grid (FNPG) is 4.08%. It proved that the use of Grid-based HOOF for analyzing movements on video data is good enough and can be improved in the future studies.

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