Reference no: EM132371503
Assignment outline
In this assignment, you need do a classification for epileptic electroencephalograph (EEG) signals using the supervised machine learning techniques you learnt in the CSC8003 course.
Background knowledge and Data description
What is Epileptic EEG Data Classification?
Epileptogenic localization is a critical factor for successful epilepsy surgery. Therefore, it is meaningful to classify the epileptic EEG Data and nonepileptic EEG Data accurately. The main process for EEG data classification can be seen in Figure 1. Our assignment only covers the classification stage.
Figure 1: EEG Data Classification
Your target in this assignment is classifying the epileptic EEG Data and nonepileptic EEG Data based on given features set.
Data description
At the beginning of the assignment, 9000 cases of patients' data are given as attachments. They include 5500 cases training data and 3500 cases testing data. For all the 9000 cases, there are 8 data sets which are feature data sets(x1, x2, x3, x4, x5, x6, x7 and x8). In addition, for each case, there is a label to show whether it is an epileptic EEG Data (S) or nonepileptic EEG Data (N).
The 8 feature data sets are calculated from the raw EEG data using 8 different feature extraction methods (First Quartile Q1, Third Quartile Q3, Q1-Q3, Standard Deviation, Min, Max, Mean and Median). However, all the feature data x1 from different cases are calculated by the same feature extracting method. So do x2, x3, x4, x5, x6, x7 and x8.
Report
In this report, the following content should be covered:
• Survey about machine learning application on epileptic EEG Data classification.
The survey should only focus on the machine learning application on feature selections and model building. Not necessary for feature extraction.
• Analyze the data sets given in this assignment, what are their features?
• According to the data sets and survey, discuss which machine learning methods you will use in this assignment and show the reasons.
You need selected two methods from the following options:
1. Decision Tree
2. K-NN
3. Neutral networking
4. Support vector machines
• Use the machine learning methods you selected to process the training data sets and briefly analysis which data set is useful for EEG Data classification. The data set here means feature set, for example, x1 feature set. In training set, the x1 feature set has 5500 values.
• According to the labels, two machine learning methods are discussed and used to do the EEG Data classification based on training data sets separately.
• The feature selection methods and model building results need to be presented in your report clearly. You need add key equations, figures or tables to present your methods and results. The programming codes and supporting figures or excel data should be presented in the appendix part of the report.
• The performance of your classification methods is evaluated based on testing data sets and their labels. The results should be presented in tables or figures.
• You should comparing the results which are obtained by two different machine learning results. Discuss the advantages and disadvantages of your two machine learning methods used in this assignment separately.
• The report includes more than 1100 words but less than 8 pages (except title page, table of content, appendix and reference list).