Unsupervised learning on EEG data

Project:

Apply unsupervised machine learning algorithms like PCA and K-Means on the given EEG dataset. Also, prepare a report based upon the following criteria(see report criteria given below).

Dataset:

A data set with 4980 segments is given in Project data set 2 (for report 2 working), in which each segment is the data collected from different patients (not in chronological order). For all the 4980 segments, there are 9 data sets (p1, p2, p3, p4, p5, p6, p7, p8 and p9) that can be considered feature sets. The feature sets are calculated from the corresponding segments of raw EEG data using 9 different feature extraction methods respectively (which are different with x1 to x8). However, all the feature data p1 from different segments are calculated by the same feature extraction method. So do p2, p3, p4, p5, p6, p7, p8 and p9.

The labels of 4980 segments were given in Project data set 3 (for report 2 testing). However, you can only use the labels to evaluate your classification results. Don’t use them for feature selection and model building.

Report criteria:

In the report, the following deliverables are expected:

• Do research on the application of unsupervised machine learning techniques in practice for DoA EEG Data classification. The research should focus on only unsupervised machine learning applications on feature selection and model building for DoA EEG data classification or index design, excluding feature extraction and background information about DoA EEG signals.

• Analyze the data sets given in Project data set 2, what are the statistics information of the data and what are the relationship among the data sets?

• According to the data sets and research outcome, discuss which unsupervised machine learning methods you will use in this assignment with appropriate justification. You need to select two unsupervised machine learning methods (e.g. K means, PCA) covered in the course content.

• Process the feature data sets by using the unsupervised machine learning methods you selected and analyze which data set is more useful for EEG Data classification.

• Two unsupervised machine learning methods are discussed and used to complete the EEG Data classification based on the selected feature data sets independently. As a result, the 4980 segments should be classified into two groups. You should label them as “A and B” instead of “Awake and Deep anesthetic” for ease of practice.

• The feature selection methods and model building results need to be presented in the report clearly, including key equations, figures and (or) tables that may help with presentation and discussion. The programming code and result in a spreadsheet including supportive figures and (or) tables should also be submitted together with the report (for the sake of description, we refer to the term “Appendix” to these materials in submission such as code and spreadsheets).

• Compare the classification results obtained from your two independent unsupervised learning methods to see if they match each other and discuss why; Compare your classification results with given labels in Project data set 3. The comparison results should be presented in tables or figures.

• Discuss the advantages and disadvantages of each unsupervised machine learning method used in this report.

Machine Learning (ML) Python

Projektin tunnus: #37232252

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