# Studies to Overcome Brain-Computer Interface Challenges
###### Abstract  
A brain-computer interface (BCI) is a promising technology that can analyze brain signals and control a robot or computer according to a user's intention. This paper introduces our studies to overcome the challenges of using BCIs in daily life. There are several methods to implement BCIs, such as sensorimotor rhythms (SMR), P300, and steady-state visually evoked potential (SSVEP). These methods have different pros and cons according to the BCI type. However, all these methods are limited in choice. Controlling the robot arm according to the intention enables BCI users can do various things. We introduced the study predicting three-dimensional arm movement using a non-invasive method. Moreover, the study was described compensating the prediction using an external camera for high accuracy. For daily use, BCI users should be able to turn on or off the BCI system because of the prediction error. The users should also be able to change the BCI mode to the efficient BCI type. The BCI mode can be transformed based on the user state. Our study was explained estimating a user state based on a brain's functional connectivity and a convolutional neural network (CNN). Additionally, BCI users should be able to do various tasks, such as carrying an object, walking, or talking simultaneously. A multi-function BCI study was described to predict multiple intentions simultaneously through a single classification model. Finally, we suggest our view for the future direction of BCI study. Although there are still many limitations when using BCI in daily life, we hope that our studies will be a foundation for developing a practical BCI system.  
definition  
responsible for their respective functions. The motor cortex's alpha wave (8-13 Hz) and beta wave (13-30 Hz) will increase or decrease according to the movement intention. For example, when users want to move their hands or feet, the power of alpha and beta waves decreases on the corresponding motor cortex [2; 5; 6; 7; 8]. Therefore, the BCI system can predict a left hand, a right hand, or feet movement intention using power change of the brain area. SMR-BCIs are usually used to control a mouse cursor or an electric wheelchair. Users can control the direction of the cursor or wheelchair to the front, left, or right according to feet, a left-hand, or a right-hand movement intention. SMR-BCIs are intuitive. However, this method requires a long training time for movement imagination [7]. P300 is a positive peak of the brain signal on the parietal region about 300 ms after the stimulation [5]. P300 is greatest upon stimulus when a user wants to select among several stimuli. Therefore, P300-based BCI can predict the target that the user wants by choosing the biggest P300 stimulus. P300-BCIs are generally used to type letters by looking at characters [9; 10; 11]. They can be used to select one among many options, although a monitor and visual stimuli are needed. SSVEP-based BCI utilizes the fact that the electroencephalography (EEG) intensity increases at the same frequency as the visual stimulus from looking from the user among visual stimuli from blinking at different frequencies [2; 12]. A user of the SSVEP-BCI can rapidly select the target among several visual stimuli. Generally, SSVEP shows the highest accuracy among the three BCI methods. SSVEP-BCIs also require a monitor and visual stimuli. The stimuli of SSVEP can make the user's eyes tired [13].  
As described above, BCIs have pros and cons according to the BCI type [14; 15]. However, all these methods are limited in choice. It means that the user of these BCIs can do only predetermined tasks such as selecting wheelchair directions or a character. The user cannot do new jobs such as drinking water or brushing teeth. This paper aims to introduce our studies to overcome the limitations of previous BCI methods for practical use.