Brain-Computer Interface (BCI) for Speech-to-Text

This project aims to develop a Brain-Computer Interface (BCI) that analyzes an individual’s EEG signals and converts them to words or sentences. The primary goal is to create a brain-to-language program for mute and speech-impaired individuals, as well as facilitate language-heavy tasks such as coding, presentations, and translation.

Table of Contents

• Requirements
• Installation
• Data Acquisition
• Preprocessing and Feature Extraction
• Training and Evaluation
• Improving the Model
• Deployment

Requirements

• Python 3.x
• MNE
• NumPy
• PyWavelets
• TensorFlow
• scikit-learn
• PyPrep
• Padasip

Installation

Install Python 3.x and the necessary libraries using pip:

pip install mne numpy pywt tensorflow scikit-learn pyprep padasip

Data Acquisition

1. Obtain a reliable and accurate EEG headset that fits your needs and budget. Some popular options include Emotiv EPOC, Muse, and OpenBCI headsets. The headset should have good documentation and API support to facilitate data acquisition.

2. Record labeled EEG data for your specific use case. This data should consist of EEG recordings for different words/sentences, along with their corresponding labels (i.e., the actual words/sentences). You can use the API provided by your EEG headset to record and save the data in a suitable format (e.g., EDF, FIF, or CSV).

Preprocessing and Feature Extraction

1. Load and preprocess the data: Use the load_and_preprocess_eeg_data function to load the raw EEG data from the recorded files and preprocess it. This function applies a series of preprocessing steps, including filtering, artifact removal, ICA, wavelet-based denoising, and adaptive noise cancellation.

2. Extract features: Use the extract_features function to extract relevant features from the preprocessed EEG data. This function calculates power spectral density (PSD) values for different frequency bands (delta, theta, alpha, beta, and gamma) and log-transforms them to create a feature vector for each data segment.

Training and Evaluation

1. Prepare the data for training: Use the prepare_data function to split the feature matrix and labels into training and testing sets. This function also standardizes the features using the StandardScaler from scikit-learn.

2. Train the neural network: Use the train_neural_network function to define and train a neural network model. This function creates a simple feedforward neural network using TensorFlow and trains it using the Adam optimizer and sparse categorical crossentropy loss function. You can adjust the architecture, optimizer, and loss function to suit your specific problem.

3. Evaluate the model: Use the model.evaluate method to evaluate the trained model on the testing set. This will give you an indication of the model’s performance on unseen data.

Improving the Model

If the model’s performance is not satisfactory, experiment with different preprocessing techniques, feature extraction methods, and neural network architectures. You can also try other machine learning algorithms, such as support vector machines (SVM), random forests, or recurrent neural networks (RNN).

Deployment

Once you have a satisfactory model, you can deploy it in real-time applications to assist mute or speech-impaired individuals. This will require integrating the model with a real-time EEG data acquisition pipeline and converting the predicted words/sentences into speech or text output.This can be achieved using text-to-speech engines or virtual keyboards for real-time communication.