Error Detection and New Stimulus Mechanisms in Brain-computer Interface
Hamza Fawzi Altakroury
Sabanci University Research Database
Brain Computer Interfaces (BCIs) constitute a research field whose moti-vation is to help disabled individuals to communicate with the environment around them directly through the electrical activity of their brain rather than by the usual muscular output mechanism of the human body. The idea of non-invasive BCI is based on collecting brain signals using medical electrodes placed on the scalp of the patient and then trying to understand what the patient is trying to do/say by automatically analysing the collected signals. In other words, BCI can be imagined as a way to compensate the damaged internal nerves that used to carry signals from the brain, by using external cables connected with the computer.
Although extensive research continues to be carried out in the field of BCI, still BCI is working only inside laboratories. This is due to the weakness of the brain signals that are acquired. It is impossible to understand always the meaning of the signals without error. The existence of errors in such systems means that it is impossible to depend totally on them to control the life of disabled individuals.
One of the well-known BCI types is called the P300 paradigm. It provides individuals with a method to choose any target only by concentrating on this target while it is ashing. The flash on the screen is considered as a stimulus for the brain, and the brain's response to this stimulus is known as the P300 signal and can be detected in the acquired signals from the brain. P300-BCI is one of the most well-known paradigms in the BCI field.
One way to reduce the number of errors in any BCI system in general, and in P300 paradigms in particular, may be by using Error-related Poten-tials (ErrP). These ErrP signals are generated when the subject detects an error in the system. Therefore, these signals could be used as a feedback for the BCI system to verify its last response. If the BCI system, for example, generates a wrong output, then an ErrP will be generated from the subject's brain which could be exploited to generate a message that the last output generated is not correct. Another way to reduce the number of errors, in the context of P300 paradigms, may be by making the neighbour non-target items have the same job of the target item. By using this idea, whether the subject gives attention to these non-target items or not, the output will be as the subject expects.
In this research, we have experimentally examined two dierent scenarios for generating ErrP signals. Having ErrP signals from two dierent scenarios makes it possible for us to see if the ErrP signals have the same characteristics under dierent scenarios. In addition, we have implemented a new P300 paradigm motivated by a BCI-based robotic control application, in which the target's neighbour items have the same job of the target itself. In this new implementation, we get better classification performance through an analysis that compensates for the change in the number of classes.