Using brain signals in establishing a communication interface is not an easy task. The bunch of challenges faced by the brain-computer interface is categorized as technical and usability. The obstacles in the system, EEG features fall under the technical category. The limitations affecting the human acceptance are described under usability. Let us discuss the challenges-
Most of the people find it hard to use the technology and to face the limitations of user acceptance of BCI technology utilization. All the issues in the training process which are needed for the discrimination of classes. ITR, Information Transfer Rate, the system evaluation metrics used to combine acceptance aspects and performance.
Using the technology needs training which is time-consuming. There are two phases of training, the classifier calibration process and the preliminary phase, and both these phases consume time. In the preliminary phase, the user has to be taught the ways of dealing the system and controlling the feedback signals of his brain. Whereas, the next level is to train the signal of the subject for learning the classifier.
Employing a single trial rather than focusing on the multi-trial analysis. The analysis helps to place the burden of small training size on subsequent components of the BCI system along with getting the value of the signal to noise ratio.
Information Transfer Rate-
This evaluation metric is used for commanding BCI systems. The metric depends on the accuracy of the target detection, a number of choices, and the average times for a selection.
- Technical Challenges-
Technical challenges are recorded in the BCI system when there is a problem with the electrophysiological properties of the brain signals. These signals are of high significance because they include non-linearity, noise, non-stationarity, and small training sets.
For limiting the technical issues in the BCI system, the solutions are spread over various components of the BCI system. Let us have a look at the proposed solutions to these problems-
For enhancing the signal caused by the external factors, preprocessing in either time, spatial or frequency domains, the signal levels are increased to improve the ratio of signal to noise.
Separability of multiple classes-
Machine learning techniques help in discrimination and identification of the selected classes. They help in overcoming some of the limitations associated with the single trail, training sets and achieving higher performance and better ITR results. Demonstrating three different machine learning algorithms- Support Vector Machine, Linear Discriminant Analysis, K Nearest Neighbors- is the next step.
The behavior of the brain and the handled activities are reflected by the brain signals. The signals also show how the received information can change the other parts of the body by sensing the organs. The interface also provides a channelling facility between external equipment and brain. Blue Eyes Technology- Intelligence Sensing System is a technology which you should give a read to for more knowledge on the topic.