## Towards Reliable Brain-Computer Interface: Achieving Perfect Accuracy by Sacrificing Time

Name

Jevgeni Savostkin

Abstract

Brain-computer interface (BCI) is a computer system for extracting brain electric

neural signals and using them to control computer applications. For the operation

BCI requires a user to concentrate on some mental tasks. Besides measuring

the signals, BCI converts raw electric signal to digital representation and maps the

data to computer commands. Unfortunately, the probability of predicting the right

command is below 100% and therefore the reliability of these systems is relatively

low.

Low reliability is a huge problem for BCI, since they will not be widely trusted

and used while the prediction accuracy is low. The existing solutions usually try

to improve the prediction accuracy of BCI without focusing too much on the time

what is required for a single user’s concentration attempt. They apply different

prediction models and signal processing techniques in order to raise the accuracy

of prediction. Our solution goes the opposite way – it tries to discover how many

concentration attempts should be done in a row (i.e how long does it take), to

guarantee the prediction accuracy of 99%.

The solution described in the thesis is based on Condorcet’s jury theorem [1].

It states that if we have two options and the chance to pick correct is larger than

50%, then, if we make several attempts in a row, the probability to pick the correct

option by majority vote is rising with the number of attempts. In this work we

apply the main Condorcet’s principle in a BCI perspective. First we develop a

system that can reach the single concentration attempt’s prediction accuracy to

be more than 50% and then we use multiple concentration attempts in a row to

improve the overall accuracy. We expect that given enough attempts we can reach

99% classification accuracy. We compare the empirical results with the theoretical

estimates and discuss them.

The BCI technology is a relatively young field. In order to fully integrate it

into our ordinary life, the contribution from scientists and engineers is required for

converting BCI to a reliable system. The following work contributes to reliability

of BCI systems.

neural signals and using them to control computer applications. For the operation

BCI requires a user to concentrate on some mental tasks. Besides measuring

the signals, BCI converts raw electric signal to digital representation and maps the

data to computer commands. Unfortunately, the probability of predicting the right

command is below 100% and therefore the reliability of these systems is relatively

low.

Low reliability is a huge problem for BCI, since they will not be widely trusted

and used while the prediction accuracy is low. The existing solutions usually try

to improve the prediction accuracy of BCI without focusing too much on the time

what is required for a single user’s concentration attempt. They apply different

prediction models and signal processing techniques in order to raise the accuracy

of prediction. Our solution goes the opposite way – it tries to discover how many

concentration attempts should be done in a row (i.e how long does it take), to

guarantee the prediction accuracy of 99%.

The solution described in the thesis is based on Condorcet’s jury theorem [1].

It states that if we have two options and the chance to pick correct is larger than

50%, then, if we make several attempts in a row, the probability to pick the correct

option by majority vote is rising with the number of attempts. In this work we

apply the main Condorcet’s principle in a BCI perspective. First we develop a

system that can reach the single concentration attempt’s prediction accuracy to

be more than 50% and then we use multiple concentration attempts in a row to

improve the overall accuracy. We expect that given enough attempts we can reach

99% classification accuracy. We compare the empirical results with the theoretical

estimates and discuss them.

The BCI technology is a relatively young field. In order to fully integrate it

into our ordinary life, the contribution from scientists and engineers is required for

converting BCI to a reliable system. The following work contributes to reliability

of BCI systems.

Graduation Thesis language

English

Graduation Thesis type

Master - Software Engineering

Supervisor(s)

Ilya Kuzovkin, Raul Vicente

Defence year

2017