Spike Sorting Algorithm
Budget: $10 – $30 USD
• The feature space obtained for each electrode after applying Principal Component Analysis (PCA) to the aligned extracted spikes of each electrode. Use two principal components. Name the figures “FeatureSpace_1_3_5.jpg” for electrode 1 and “FeatureSpace_2_3_5.jpg” for electrode 2 for threshold of 3.5 times the standard deviation, and “FeatureSpace_1_5.jpg” for electrode 1 and “FeatureSpace_2_5.jpg” for electrode 2 for threshold of 5 times the standard deviation.
• A text file stating the number of clusters that you identified by visual inspection of the feature space of each electrode. Name the file “Number of Neurons_3_5.txt” for threshold of 3.5 times the standard deviation and “Number of Neurons_5.txt” for threshold of 5 times the standard deviation.
• A figure showing the first 20,000 samples of the raw data of each channel with an “*” marking the detected spikes colored with different colors depending on the neuron each spike belongs to. Name the figures “DetectedSpikes_1_3_5.jpg” for electrode 1 and “DetectedSpikes_2_3_5.jpg” for electrode 2 for threshold of 3.5 times the standard deviation, and “DetectedSpikes_1_5.jpg” for electrode 1 and “DetectedSpikes_2_5.jpg” for electrode 2 for threshold of 5 times the standard deviation.
• A figure showing the average spike of each neuron colored with different colors. Name the figure “Templates_3_5.jpg” for threshold of 3.5 times the standard deviation, and “Templates_5.jpg” for threshold of 5 times the standard deviation.
Note: You can use the PCA function available in Python. No need to re-implement it.
• A text file stating the number of clusters that you identified by visual inspection of the feature space of each electrode. Name the file “Number of Neurons_3_5.txt” for threshold of 3.5 times the standard deviation and “Number of Neurons_5.txt” for threshold of 5 times the standard deviation.
• A figure showing the first 20,000 samples of the raw data of each channel with an “*” marking the detected spikes colored with different colors depending on the neuron each spike belongs to. Name the figures “DetectedSpikes_1_3_5.jpg” for electrode 1 and “DetectedSpikes_2_3_5.jpg” for electrode 2 for threshold of 3.5 times the standard deviation, and “DetectedSpikes_1_5.jpg” for electrode 1 and “DetectedSpikes_2_5.jpg” for electrode 2 for threshold of 5 times the standard deviation.
• A figure showing the average spike of each neuron colored with different colors. Name the figure “Templates_3_5.jpg” for threshold of 3.5 times the standard deviation, and “Templates_5.jpg” for threshold of 5 times the standard deviation.
Note: You can use the PCA function available in Python. No need to re-implement it.