# Review on BCI Virtual Rehabilitation and Remote Technology Based on EEG for Assistive Devices
## 4 Source of EEG Information and Data Handling
### BCI Competition III  
This is a collection of public datasets. Each dataset has an identification number and different characteristics. According to the official site, the goal of the \"BCI Competition III is to validate signal processing, and classification methods for Brain-Computer Interfaces (BCIs)\" [21]. The differences between the information of each dataset are the reason that is used in several investigations. Some of the most used datasets for BCI are described here:  
* Dataset II (P300 speller paradigm). This set is a record of P300 evoked potentials focused on predicting a character out of 36 different characters (a 6-6 matrix). It comprises 36 classes, 64 EEG channels (0.1-60 Hz), 240 Hz sampling rate, 85 training trials, and 100 test trials recorded with the BCI2000 system. * Dataset IIIa (motor imagery, multi-class). This dataset measures the kappa-coefficient motor imagery task with four classes (left hand, right hand, foot, tongue) from 3 subjects. It comprises four classes, 60 EEG channels (1-50 Hz), 250 Hz sampling rate, and 60 trials per class. * Dataset IIIb (motor imagery with the non-stationary problem). Dataset IIIb consists of the three recordings S4, X11, and O3 VR with 1080, 1080, and 640 trials. Due to a mistake, trials 1-160 and 161-320 in O3 VR are equal. This means that in 2 \(\times\) 75 (out of 640) trials, one can obtain the test set labels by looking at the label of the corresponding trials. In order to remove the repetition of testing data, the first 160 trials (containing another 43 testing trials) will not be considered in the final evaluation. Accordingly, the number of testing trials in O3 VR will be reduced to 202 (out of 480). * Dataset IVa (Motor imagery, small training sets). This set is focused on motor imagery tasks with two classes (right hand, foot) from 5 subjects; for two subjects, most trials are labeled (80% and 60%, respectively), and for the other three, less and less training data are given (30%, 20%, and 10%, respectively). It was created to probe the EEG classification with small data. The general composition is two classes, 118 EEG channels (0.05-200 Hz), 1000 Hz sampling rate, and 280 trials per subject. * Dataset IVb (Motor imagery, uncued classifier application). It is training data for motor imagery with two classes (left hand, foot) from one subject, with continuous data intended to specify when the mental state changes from movement to movement. The labels are: \(-1\) for the left hand, 1 foot, and 0 rest averaged over all samples. This is from 118 EEG channels (0.05-200 Hz), 1000 Hz sampling rate, 210 training trials, and 12 min of continuous EEG for testing. As mentioned before, these datasets were used in different situations, according to the intention of the research. For example, Dataset IV (BCI III IV) was used in [22] and the Physionet dataset was used in [23], as can be seen in Table 1.  
Besides the described databases, there are some others available in the public sector that were designed under MI conditions. Some of these databases are used in other signal processing studies. For example, there exists BCI IV that is a composed of five datasets. Three of those data sources are focused on EEG signals with MI conditions. The study [22] used this source of information as part of a signal classification process for BCI creation. Other public available datasets are the GigaScience Database [35], which contains MI information on 52 subjects; the Grasp and Lift EEG Challenge [36] which contains information of 12 subjects and 10 series of trials of 6 motor tasks; and the High Gamma dataset that was used in [37]. In addition, there are some public EEG datasets focused on other paradigms and signals that can be mixed with the records of other types of signals (such as EMG).