Technical report: Cost-benefit analysis of cooking banana seed propagation methods

<p>Confusion matrix obtained as a result of Bayesian classification of EEG patterns, corresponding to various eye movements and blinking, after EOG artifact removal.</p

Brain-Computer Interface Based on Generation of Visual Images

This paper examines the task of recognizing EEG patterns that correspond to performing three mental tasks: relaxation and imagining of two types of pictures: faces and houses. The experiments were performed using two EEG headsets: BrainProducts ActiCap and Emotiv EPOC. The Emotiv headset becomes widely used in consumer BCI application allowing for conducting large-scale EEG experiments in the future. Since classification accuracy significantly exceeded the level of random classification during the first three days of the experiment with EPOC headset, a control experiment was performed on the fourth day using ActiCap. The control experiment has shown that utilization of high-quality research equipment can enhance classification accuracy (up to 68% in some subjects) and that the accuracy is independent of the presence of EEG artifacts related to blinking and eye movement. This study also shows that computationally-inexpensive Bayesian classifier based on covariance matrix analysis yields similar classification accuracy in this problem as a more sophisticated Multi-class Common Spatial Patterns (MCSP) classifier

General scheme of an EEG-based BCI.

<p>EEG is recorded by electrodes placed on the scalp and digitized by an ADC. Computer processing extracts features most suitable for identifying the subject's intensions. When intension is classified, a certain command is sent to an external device (e.g., a display). Feedback provides the subject with results of his actions thus allowing him to adapt to the system behavior.</p

Classification quality for all subjects during training and test sessions, as measured by value .

<p>Classification quality during training is displayed on left panes A and C, quality during test is presented on panes B and D. The first row of columns corresponding to the 4^th day (4a) represents values computed from data for 16 EEG electrodes, and the second one (4b) represents the values computed from data for all 24 EEG electrodes. Notice that each column exceeds the level <i>p</i> = 0.33 related to random classifying.</p

Electrode locations for EEG headsets used in this study.

<p>EPOC (A), ActiCap (B).</p

Decrease of total variance of signals after sequential removal of the independent components for all subjects.

<p>Left pane (A) represents signals recorded by EOG electrodes, and right pane (B) corresponds to EEG electrodes. Removed artifact components are marked by red points.</p

Spatial distributions of individual ICA components related to EOG artifacts.

<p>Graphs correspond to blinking (B), moving eyes upwards (U), to the right (R), downwards (D) and to the left (L).</p

Schematic illustration of experiment protocol and each session timing.

<p>Sequence of sessions (A), structure of each training (test) session block (B), and structure of auxiliary session (C) are presented. Warnings are marked by blue and instructions to execute each task are marked by green. Instruction durations are given in seconds. Within each block, the instructions to imagine the face or the house are placed in random order, and each instruction is presented twice in a block.</p

Comparison of EEG pattern recognition quality for the training and the test sessions, before and after EOG artifact removal.

<p>Comparison of EEG pattern recognition quality for the training and the test sessions, before and after EOG artifact removal.</p

Quality of classification measured by index .

<p>Data representation is the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020674#pone-0020674-g004" target="_blank">Figure 4</a>.</p