Dysphoria detection using EEG signals

Dysphoria is a state faced when one experienced disappointment. If it is not handled properly, dysphoria may trigger acute stress, anxiety and depression. Typically, the individual who experienced dysphoria are in-denial because dysphoria is always being associated with negative connotations such as incompetency to handle pressure, weak personality and lack of will power. To date, there is no accurate instrument to measure dysphoria except using questionnaire by psychologists, such as: Depression, Anxiety and Stress Scale (DASS) and Nepean Dysphoria Scale (NDS-24). Participants may suppress or exaggerate their answers resulting in misdiagnosis. In this work, a theoretical Dysphoria Model of Affect (DMoA) is developed for dysphoria detection. Based on the hypothesis that dysphoria is related to negative emotion, the input from brain signal is captured using electroencephalogram (EEG) device to detect negative emotions. The results from analyzing the EEG signals were compared with DASS and NDS questionnaires for correlation analysis. It is observed that the proposed DMoA approach can identify negative emotions ranging from 55% to 77% accuracy. In addition, the NDS questionnaire seems to provide better distinction for dysphoria as compared to DASS and is similar to the result yielded by DMoA in detecting dysphoria. Thus, DMoA approach can be used as an alternative for early dysphoria detection to assist early intervention in identifying the patients’ mental states. Subsequently, DMoA approach can be implemented as another possible solution for early detection of dysphoria thus providing an enhancement to the present NDS instruments providing psychologists and psychiatrists with a quantitative tool for better analysis of the patients’ state

Pengembangan Laboratorium Virtual sebagai Media Pembelajaran: Peluang dan Tantangan

Abstract: Laboratory is a learning resource and learning media. The purpose of this research is what is the urgency of the laboratory as a learning medium and what are the opportunities and challenges of the virtual laboratory as a pedagogical framework overview. Therefore, researchers are interested in conducting studies on this matter. This research uses library research and is qualitative in nature. The result of this research is a laboratory as a place for activities that are needed in practice, which is often used as a standard for student success. Virtual laboratories have a significant impact in terms of preparing students for real-world experiences, as well as savings in equipment procurement and maintenance costs, flexibility in location, study time and practical use Abstrak: Laboratorium merupakan salah satu sumber belajar dan media pembelajaran. Tujuan dari penelitian ini adalah bagaimanakah urgensi laboratorium sebagai media pembelajaran dan bagaimanakah peluang dan tantangan laboratorium virtual sebagai sebuah pedagogical framework overview. Oleh karena itu peneliti tertarik untuk melakukan kajian mengenai hal ini. Penelitian ini menggunakan jenis penelitian kepustakaan dan bersifat kualitatif. Hasil penelitian ini adalah laboratorium sebagai tempat kegiatan yang dibutuhkan dalam praktek, seringkali dijadikan standar kesuksesan mahasiswa. Laboratorium virtual mempunyai dampak signifikan dalam hal mempersiapkan mahasiswa untuk menghadapi pengalaman nyata, juga penghematan biaya pengadaan dan perawatan alat, fleksibilitas lokasi, waktu belajar dan praktek

Rancang Bangun Alat Pendeteksi Emosi Pada Anak Menggunakan Metode K-Means

 Emosi merupakan suatu perasaan intens yang ditujukan kepada seseorang atau suatu hal yang dapat mendorongnya untuk melakukan suatu tindakan atau berekspresi yang dapat dipicu dari dalam atau luar dirinya. Dalam kehidupan sehari-hari sangat penting untuk memahami kondisi emosional seseorang. Emosi seseorang dapat diketahui salah satunya dari ekspresi wajah, namun terkadang seseorang dapat memanipulasi apa yang sedang dialaminya dengan cara mengendalikan ekspresi wajahnya. Oleh karena itu dirancanglah sebuah alat untuk mendeteksi emosi seseorang berdasarkan perubahan kondisi tubuhnya. Pada penelitian ini akan digunakan dua buah sensor yaitu sensor GSR dan juga sensor Heart Rate. Output yang diperoleh dari masing-masing sensor tersebut akan diolah datanya dengan menggunakan mikrokontroler atau Arduino. Selanjutnya hasil output-nya akan ditampilkan berupa text dengan menggunakan LCD. Data yang akan ditampilkan pada LCD tersebut akan di cluster terlebih dahulu menjadi beberapa kelompok menggunakan metode K-Means. Dari hasil pengelompokkan jenis emosi dengan menggunakan metode K-Means masih terdapat perbedaan hasil uji antara alat dan prediksi psikolog. Dari 30 data terdapat 5 data yang berbeda atau bisa dikatakan terdapat perbedaan sebesar 16%. Alat ini juga dibuat portable dengan adanya alat pendeteksi emosi pada anak ini dapat membantu para orang tua dan para guru untuk antisipasi perubahan emosi tersebut terutama pada seorang anak

EVOKE: Emotion Enabled Virtual Avatar Mapping Using Optimized Knowledge Distillation

As virtual environments continue to advance, the demand for immersive and emotionally engaging experiences has grown. Addressing this demand, we introduce Emotion enabled Virtual avatar mapping using Optimized KnowledgE distillation (EVOKE), a lightweight emotion recognition framework designed for the seamless integration of emotion recognition into 3D avatars within virtual environments. Our approach leverages knowledge distillation involving multi-label classification on the publicly available DEAP dataset, which covers valence, arousal, and dominance as primary emotional classes. Remarkably, our distilled model, a CNN with only two convolutional layers and 18 times fewer parameters than the teacher model, achieves competitive results, boasting an accuracy of 87% while demanding far less computational resources. This equilibrium between performance and deployability positions our framework as an ideal choice for virtual environment systems. Furthermore, the multi-label classification outcomes are utilized to map emotions onto custom-designed 3D avatars.Comment: Presented at IEEE 42nd International Conference on Consumer Electronics (ICCE) 202

Generating Visual Stimuli from EEG Recordings using Transformer-encoder based EEG encoder and GAN

In this study, we tackle a modern research challenge within the field of perceptual brain decoding, which revolves around synthesizing images from EEG signals using an adversarial deep learning framework. The specific objective is to recreate images belonging to various object categories by leveraging EEG recordings obtained while subjects view those images. To achieve this, we employ a Transformer-encoder based EEG encoder to produce EEG encodings, which serve as inputs to the generator component of the GAN network. Alongside the adversarial loss, we also incorporate perceptual loss to enhance the quality of the generated images

Fusi Algoritma K-Means dan CNN untuk Klasifikasi Emosi pada Anak

Emosi adalah perasaan yang diarahkan pada seseorang ataupun sesuatu yang bisa menyebabkan sesorang bertindak atau mengekspresikan diri dan dapat dipicu secara internal ataupun eksternal. Ekspresi wajah merupakan salah satu cara termudah untuk mengetahui emosi seseorang, namun terkadang seseorang dapat mengontrol dan memanipulasi ekspresi wajah mereka sehingga tidak sesuai dengan apa yang dialami. Oleh karena itu, penelitian ini mengembangkan sistem yang dapat mengidentifikasi emosi anak tidak hanya berdasarkan wajah tetapi juga berdasarkan perubahan kondisi tubuhnya. Penelitian ini menggunakan metode klasifikasi Convolutional Neural Network (CNN) dan juga metode clusterisasi K-Means. Penggunaan 2 metode pada penelitian ini berfungsi untuk memperkuat akurasi sistem. Metode K-Means digunakan untuk mengidentifikasi emosi berdasarkan detak jantung dan konduktivitas kulit sedangkan Metode CNN digunakan untuk mengidentifikasi emosi berdasarkan ekspresi wajah. Hasil yang diperoleh dari kedua metode tersebut akan diproses menggunakan metode fusi yang aturannya disesuaikan berdasarkan hasil pengamatan dan pengukuran, sehingga dapat diprediksi emosi pada anak berdasarkan parameter detak jantung, ekspresi wajah, dan konduktivitas kulit. Anak dengan umur 6 hingga 12 tahun digunakan sebagai subjek pada penelitian ini. Dari penelitian ini berhasil didapatkan hasil prediksi emosi anak dengan akurasi keberhasilan sebesar 80%

Predicting the Standard and Deviant Patterns In EEG Signals Based On Deep Learning Model

In the recent years, there has been a significant growth in the area of brain computer interference. The main aim of such area is to read the brain activities, formulate a specific/desired output and power a specific approach using such output. Electroencephalography (EEG) may provide an insight into the analysis procedure of the human behavior and the level of the attention. Using the deep learning based neural network has a great success in different applications recently,such as making a decision, classifying a pattern and predicting an outcome by learning from a set of data and build the right weight matrices to represent the prediction outcome or the learning patterns. This research work proposes a novel model based on long short-term memory network to predict the standard and the deviant cases within EEG data sets. The EEG signals are acquired utilizing all the 128 electrodes that represent the 128 channels from infants aged between 5 and 7 months. Statistical approaches, principal component analysis (PCA) and autoregressive (AR) power spectral density estimate have been employed to extract the features from the EEG data sets. The proposed deep learning based model has shown great robustness dealing with different types of features extracted from the processed data sets. Very promising results have been achieved in predicting the standard and deviant cases. The standard case was presented with frequent, repetitive stimulus and the deviant case was presented with infrequent sounds

Cognitive behaviour analysis based on facial information using depth sensors

Cognitive behaviour analysis is considered of high impor- tance with many innovative applications in a range of sectors including healthcare, education, robotics and entertainment. In healthcare, cogni- tive and emotional behaviour analysis helps to improve the quality of life of patients and their families. Amongst all the different approaches for cognitive behaviour analysis, significant work has been focused on emo- tion analysis through facial expressions using depth and EEG data. Our work introduces an emotion recognition approach using facial expres- sions based on depth data and landmarks. A novel dataset was created that triggers emotions from long or short term memories. This work uses novel features based on a non-linear dimensionality reduction, t-SNE, applied on facial landmarks and depth data. Its performance was eval- uated in a comparative study, proving that our approach outperforms other state-of-the-art features