Robotics and Autonomous Systems

Eduardo Ianez, José Maria Azorin, Andrés Ubeda, José Manuel Ferrandez, Eduardo Fernandez

Robotics and Autonomous Systems, 58, 2010

Summary

                  Brain Computer Interface (BCI) methods allow operators to generate control commands through EEG signals (Electroencephalography), through the registration of “mental tasks”.

Brain activity product electrical activity, magnetic signals (measured through magnetoencephalography, MEG) electrical signals and metabolic signals (measured through the Positron Emission Tomography, PET, or by Functional Magnetic Resonance Imaging, fMRI). Techniques are divided in invasive and evasive, in the first case microelectrodes are implanted directly in the brain (used for animals and considered to be more precise), while in the second case are only having cutaneous contact (used for human application being ethically preferred).

BCI can further be divided in synchronous and asynchronous, the first when check the EEG and take the decision, taking a certain amount of time, the second allowing actions to be thought any time and therefore it’s chosen by the authors. The proposed BCI uses Wavelet Transform to extract the relevant characteristics of the EEG, with a new Linear Discriminant Analysis (LDA) for classifying the different EEG patterns.

The experiments requires 16 scalp electrodes, with 9 concentrated on the top center, the rest are supporting preprocessing. The information arriving from the Wavelet Transform is then processed by the LDA classifier, which is based on Fisher linear discriminant, using statistics and automatic learning to find the best linear combination, the method guarantees the maximum separability between classes, but four different models are required for simultaneous classification among the three different classes (rest state, right mental tasks and left mental tasks). Model 1 implies class 1 being Right side and class 2 left side and rest state, model 2 has class one being the left side and class 2 being the right and rest state, model 3 has class 1 being the rest state and class two the right and left state and model 4 has class 1 being the right state and class two the left state.

For the four models then a decision system is created according on to the following rule: for the models from 1 to 3 (task involving the brain’s right side) one point is assigned if the vector belongs to class 1, 0.5 if it belongs to class 2 and 0 in the uncertainty region; for region 4 one point is assigned either if the vector belongs to class 1 or 2 and 0 if in uncertainty (the algorithm is shown at page 1251).

A test has been performed, experimenting simulation offline (moving a dot), online and practice online (moving a FANUC LR Mate 200iB robotic arm with 6 d.o.f.).

In the offline more errors are tracked and later on, on the same session, users performed other 50s activity online, the third session see the use of the robotic arm obtaining final scores with the method previously introduced.

The offline results show clearly that it is the worst way of performing tasks, it results in greater errors and success percentage is therefore low, since the user doesn’t receive any feedback from the system

The online method, which uses Matlab interface, appears to be more effective with less error reported, the problem is that EEG signals are time invariant, reason for which there are wrong adjustments of data at initial calculations. It has been demonstrated that through training the final results achieved are much better.

The robot arm, programmed using C++, appears to be controlled well, having the same problems of the online simulation, being the EEG time invariant.

Key Concepts

EEG control systems

Key Results

The paper has introduced the different steps involving BCI with Wavelet Transform, LDA-based classifier and in the end the algorithm for decision making. For future implementations the time invariance of EEG signals has to be adjusted and more degrees of freedom should be added to command the robotic arm. The method proposed it thought mainly for people with some physical disabilities.