[Paper Presentation] EMOTIONAL STRESS DETECTION USING DEEP LEARNING
The idea is to spot people suffering from prolonged stress and how to offer a solution for the long term sufferers by predicting and analyzing their emotions using brainwaves recorded through Neurosky Brainwave Headset. The body produces larger quantities of the chemical cortisol and they trigger an increased heart rate, heightened muscle preparedness, sweating and alertness. Emotional stress is a primary factor to the six leading causes of death. It is a feeling of emotional or physical tension that makes a person feel frustrated, angry and nervous. They can be positive when it helps to avoid risk or meet a deadline. But when the stress lasts for a long time, it may ruin our health. To refrain from this situation, the individuals recognized with stress pattern are asked to listen to soft music and the brainwave pattern is recorded in response. We use neural network architectures with attention mechanism to identify the pattern and predict the emotional state of a person.
CONSUMER EEG DEVICES
▪ 1 to 6 channels based on the device
▪ Dry electrodes makes it highly portable
▪ Wireless data collection
▪ Easy integration for programming
▪ Mostly focuses on Pre-Frontal cortex which is responsible for
▪ Decision making
▪ Emotion management
▪ Coordinating complex behavior
▪ This presentation and the related paper are the works of individuals based on
personal interest. None of the organizations that these individuals work for are
responsible for the accuracy and completeness of the content or results. This work
and presentations are for information purposes only.
▪ Single channel (FP1) EEG device
▪ Collected data from 10 individuals, multiple episodes
▪ Binary labels – ( Stressed, Relaxed )
▪ Only Raw signal is used for this experiment
Public data is used to pretrain the network and experimented in embedding the signals.
PRE-PROCESSING RAW EEG
▪ Raw EEG consists of signals from different sources
▪ Muscle movements
▪ Eye movements
▪ Apply FFT / Butterworth band pass filters to separate out brain signals.
▪ EEG Lab
▪ MNE Tools
▪ Baseline the signal
▪ EEG Patterns vary with Age and hence it needs to be considered as a feature.
DEEP LEARNING FOR EEG
▪ Pretrain the network with a public dataset (
we used one from Kaggle )
▪ Data from FP1 and FP2 ( public data) are
averaged to form FPZ
▪ Select pre frontal points and average it, if the
data contains multi channel
▪ Classifier trained to identify emotions like
“dominance”, “high arousal”, “like” etc.
▪ Data collected from Neurosky is at 512 HZ
▪ Each reading is of size 7500
▪ Raw signals, without FFT is provided as an
▪ Limited architecture to compensate low
▪ Model Learnt to ignore eye blinks and other noises
▪ However pure LSTM model failed to focus on the right patterns
▪ Adding attention layer improved accuracy drastically.
Metrics LSTM without
Accuracy 55 % 85 %
F1 score 55 % 85 %
Recall 55 % 85 %
Precision 55 % 85.42 %
Average accuracy Relaxed Stressed
LSTM without Attention 43.75 62.5
LSTM with Attention 89.47 80.952
▪ Convolution layers to autofocus and reduce computational cost
▪ Signal Embedding is not considered however we believe it will improve performance
▪ More data collection with more subjects and standardized stimuli
▪ Multi Modal networks with inputs from ECG, GSR and EEG
▪ Time synchronized data consolidation from public sources.
▪ Custom music composer to reduce stress ( based on
▪ Emotion and word prediction from EEG