Hi Sachin,
To implement a Hidden Markov Model for classifying EEG signals in MATLAB, refer the following steps:
- Load and Preprocess the EEG Data. Perform filtering, artifact removal and segmentation of the EEG Data.
- Extract relevant features from the EEG signals such as the power spectral density, wavelet coefficients and other statistical features.
- Define the number of states and other required HMM parameters like the transition matrix, emission matrix to name a few.
- Use the 'hmmtrain' function to train the HMM on the training data. Experiment with different HMM parameters to optimize the performance.
- Classify using the trained HMM with the 'hmmdecode' or 'hmmviterbi' functions to classify the EEG signals
The approach described for using HMMs is suitable for independent sequences. For continuous time series data, you can consider assigning a single state per class, which simplifies estimation. Alternatively, use a Hierarchical HMM where top-level states represent classes and lower-level HMMs model temporal variations within classes. Another option is to use a discriminative model like Conditional Random Fields, which can incorporate complex features and directly address the classification problem. For more information on implementing HMM for EEG signals, refer the following resources:
- hidden Markov models: https://www.mathworks.com/help/stats/hidden-markov-models-hmm.html
- hmmtrain: https://www.mathworks.com/help/stats/hmmtrain.html
- hmmviterbi: https://www.mathworks.com/help/stats/hmmviterbi.html
- classification by HMMs: https://www.mathworks.com/matlabcentral/fileexchange/72594-tutorial-for-classification-by-hidden-markov-model
- Training HMMs for classification: https://stats.stackexchange.com/questions/91290/how-do-i-train-hmms-for-classification
