EEG-based Emotion Classification using NN

Steps for classification:

Data Loading and Exploration:

• Imported necessary libraries (NumPy, Pandas, Matplotlib, Seaborn, Plotly, SciPy, TensorFlow).
• Loaded the emotion dataset from a CSV file ('emotions.csv').
• Displayed the first 5 rows of the dataset.

Label Conversion:

• Converted textual emotion labels ('NEGATIVE', 'NEUTRAL', 'POSITIVE') to numerical values (0, 1, 2).

Emotion Distribution Visualization (Pie Chart):

• Counted the occurrences of each emotion.
• Created a pie chart to visualize the distribution of emotions.

Time-Series Visualization:

• Plotted a sample of EEG time-series data.

Spectral Analysis (Power Spectral Density):

• Used Welch's method to calculate the power spectral density.
• Plotted the power spectral density.

Correlation Heatmap:

• Calculated the correlation matrix.
• Visualized the correlation matrix using a heatmap.

t-SNE Visualization:

• Applied t-distributed Stochastic Neighbor Embedding (t-SNE) for dimensionality reduction.
• Visualized the data in a 2D scatter plot.

Feature Significance Analysis:

• Performed t-tests to identify significant features for each emotion.
• Visualized the number of significant and non-significant features for each emotion using a bar chart.

Advanced Preprocessing:

• Normalized the data using z-score normalization.

Data Splitting:

• Split the data into training and testing sets.

Neural Network Model Building:

• Built a neural network model using TensorFlow and Keras.
• Compiled the model with the Adam optimizer and sparse categorical crossentropy loss.

Confusion Matrix: