This data arises from a large study to examine EEG correlates of genetic predisposition to alcoholism. It contains measurements from 64 electrodes placed on subject's scalps which were sampled at 256 Hz (3.9-msec epoch) for 1 second.
There were two groups of subjects: alcoholic and control. Each subject was exposed to either a single stimulus (S1) or to two stimuli (S1 and S2) which were pictures of objects chosen from the 1980 Snodgrass and Vanderwart picture set. When two stimuli were shown, they were presented in either a matched condition where S1 was identical to S2 or in a non-matched condition where S1 differed from S2.
Each trial is stored in its own file and will appear in the following format.
trial number sensor position sample num sensor value subject identifier matching condition channel name time
0 FP1 0 -8.921 a S1 obj 0 co2a0000364 0
0 AF8 87 4.14 a S1 obj 33 co2a0000364 0.33
The columns of data are:
the trial number,
sensor position,
sample number (0-255),
sensor value (in micro volts),
subject identifier(Alcoholic(a) or Control (c)),
matching condition(a single object shown (S1 obj), object 2 shown in a matching condition (S2 match), and object 2 shown in non matching condition (S2 nomatch)),
channel number(0-63),
name(a serial code assigned to each subject),
time(inverse of sample num measured in seconds))
Subject Identifer is our label of the data, the dataset was divided into 75-25 ratio using train_test split . I used 5 algorithms on my dataset and compared their accuracy:-
- Logistic Regression 55.78%
- Naive Bayes 55.46%
- K-Nearest Neighbors 66.81%
- Decision Tree 59.11%
- Random Forest 58.12%
We further used Confusion Matrix to evaluate performance of our algorithms. Since K-Nearest Neighbors has the highest accuracy,we will use K-Nearest Neighbors on our data.