G’day again! I’ve got data… and it is beautiful!
More on this below… I am pleased to update my progress on my BYB project, Human EEG visual decoding!
Since my first blog post, I have collected the data from 6 subjects with the stimulus presentation program I developed. The program presents 5 sets of 30 images from 4 categories (Face, House, Natural Scene, Weird pictures). Since the images are randomized, I have small, color-coded blocks in the corner of each image which I use to record which stimulus is presented when.
I needed to build the light sensor to read the signals from these colored block. I used a photoresistor at first, however, there was some delay on the signal, so I decided to use photodiodes which had a faster response. Since I do not have an engineering background, I had to learn how to read circuits and to solder to build the light sensor. This was new territory for me, but it was very interesting and motivating. After building up my device, I collected data from 6 subjects from 5 brain areas (CPz, C5, C6, P7, P8) that are thought to be important in measuring brain signals related to visual stimulus interpretation.
Figure1. Data recorded from DIY EEG gear. 5 channels from 5 brains areas (orange, red, yellow, light green, green) and 1 channel from photoresistor (aqua) that was replaced by photodiode
Figure2. A circuit for photodiode(top) and the photodiodes I built (bottom)
Figure3. Checking each channel from the Arduino. One channel (Yellow) on the back of the brain is detecting alpha waves – 10 Hz waves
Figure4. Spencer (top/mid) and Christy(bottom), our coolest interns, participating in the experiment
With the raw EEG data collected from each subject, I averaged them to get the ERP (Event Related Potential) to observe what the device detected from the data. ERPs provide a continuous measure of processing between a stimulus and a response, making it possible to determine which stages are being affected by a specific experimental manipulation, and also provide excellent temporal resolution—as the speed of ERP recording is only constrained by the sampling rate that the recording equipment can feasibly support, Thus, ERPs are well suited to research questions about the speed of neural activity.
Then I performed Monte Carlo simulations to verify the statistical significance of the spikes in ERP data. Monte Carlo simulation is a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. With 100 random samples for each category, the analysis indicated that we had statistically significant spikes across the graph, especially in N170 in face images, which was very meaningful for my research. N170 is a component of the event-related potential (ERP) that reflects the neural processing of faces, which supported that we have good detection on faces across subjects compared to other categories.
Figure5. ERP data from 6 subjects for each category of images. Significant response in N170 (negative peak after 170 ms after the stimuli presentation) is detected in the face
After verifying the statistical significance of the data, I used k-means clustering, a method of vector quantization that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. K-means clustering indicated that difference between subjects was more significant than the difference between trials and that the difference between trials was more significant than the difference between categories. And, much to my excitement, it was obvious that the response to faces was distinguished from other categories across the number of averaging data sets.
With the insights from k-means clustering, I finally performed the machine learning techniques I’d been studying to analyze my accuracy at classifying what category of images people were looking at during the experiment by looking at the raw data. I performed the most popular pattern classifiers such as “linear support vector machine,” “quadratic support vector machine,” “cubic support vector machine,” “complex trees,” “gaussian,” “knn,” and so on… I used these methods on a single subject and a set of 6 subjects with and without averaging every 5, 10, 15, 20, 25, 30, 50, 75, 150 vectors of EEG data. Support Vector Machine showed the best performance among other classifiers with more than 50% of accuracy for each class with averaging data showing the better performance as expected.
Figure6. K-means clustering results with averaging every 5, 10, 20, 50 75 vectors of the EEG data for a single subject(first 2 graphs) and 6 subjects(last 2 graphs). Y axis indicates 4 categories of the images (1: Face, 2: House, 3: Natural Scene, 4: Weird pictures), further illustrated by the red lines. The graphs from 6 subjects indicate that combining multiple subjects introduces too much variation to identify faces within the group. However, the graphs from a single subject indicate that face can be distinguished from other three categories.
Again, with the data from k-means clustering, and the Machine Learning classifiers I mentioned before, I then applied a 5-fold cross validation with and without averaging every 5 EEG data. In 5-fold cross validation, each data set is divided into five disjoint subsets, where four subsets are used as training sets and the rest are used for a test set. SVM showed the best performance among other classifiers with more than 50% of accuracy for each class with averaging data showing the better performance as expected.
Figure7. The results from pattern classification with SVM . Both one subject and 6 subjects achieved good results with averaging every 5 vectors of the EEG data, producing a better result than without averaging, and data from single subject producing a better result than 6 subjects. (The darker the green down the diagonal the better, that’s the accuracy of predicting specific classes)
So now I am working on real time pattern classification so that I can detect what people are looking at without averaging multiple sets of data. I will perform spectral decomposition to compute and downsample the spectral power of the re-referenced EEG around each trial. The spectral features from all of the electrodes will be concatenated and used as inputs to pattern classifiers. The classifiers were trained using various pattern classifiers to recognize when each stimulus category is processed as the target image in real time; a separate classifier will be trained for each combination of stimulus category and time bin. Next, the trained classifiers will be used to measure how strongly the prime distractor image is processed on each trial. Finally, subjects’ RTs (to the probe image) on individual trials will be aligned to the classifier output from the respective trials.
The successful result of this research will make this kind of neural decoding accessible for any neuroscience researcher with an affordable EEG rig and provide us an opportunity to bring state-of-art neurotechnology techniques, such as brain authentication, to life. Please keep an eye on my project and feel free to ask any question. Toodle-oo!