Optimized EEG based mood detection with signal processing and deep neural networks for brain-computer interface
Electroencephalogram (EEG) is a very promising and widely implemented procedure to study brain signals and activities by amplifying and measuring the post-synaptical potential arising from electrical impulses produced by neurons and detected by specialized electrodes attached to specific points in the scalp. It can be studied for detecting brain abnormalities, headaches, and other conditions. However, there are limited studies performed to establish a smart decision-making model to identify EEG's relation with the mood of the subject. In this experiment, EEG signals of 28 healthy human subjects have been observed with consent and attempts have been made to study and recognise moods. Savitzky-Golay band-pass filtering and Independent Component Analysis have been used for data filtration.Different neural network algorithms have been implemented to analyze and classify the EEG data based on the mood of the subject. The model is further optimised by the usage of Blackman window-based Fourier Transformation and extracting the most significant frequencies for each electrode. Using these techniques, up to 96.01% detection accuracy has been obtained.
Code (0)
등록된 구현이 없습니다.
Tasks
Brain Computer InterfaceDecision MakingEEGElectroencephalogram (EEG)Similar Papers 제목 키워드 기반
Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Subject Emotion Recognition
Emotion recognition based on Electroencephalography (EEG) has gained significant attention and diversified development in fields such as neural signal processing and affective computing. However, the unique brain anatomy…
AnatomyEEGEmotion RecognitionUnityMusic Mood Detection Based On Audio And Lyrics With Deep Neural Net
We consider the task of multimodal music mood prediction based on the audio signal and the lyrics of a track. We reproduce the implementation of traditional feature engineering based approaches and propose a new model ba…
Multimodal Emotion RecognitionMusic Emotion RecognitionPredictionEEG Based Generative Depression Discriminator
Depression is a very common but serious mood disorder.In this paper, We built a generative detection network(GDN) in accordance with three physiological laws. Our aim is that we expect the neural network to learn the rel…
EEGExamining the Role of Mood Patterns in Predicting Self-Reported Depressive symptoms
Depression is the leading cause of disability worldwide. Initial efforts to detect depression signals from social media posts have shown promising results. Given the high internal validity, results from such analyses are…
DiagnosticA positive mood, a flexible brain
Flexible reconfiguration of human brain networks supports cognitive flexibility and learning. However, modulating flexibility to enhance learning requires an understanding of the relationship between flexibility and brai…