# Studies to Overcome Brain-Computer Interface Challenges
## 3 Discussion  
Over the past decades, there have been lots of BCI studies. They were usually focused on the methods to improve the prediction accuracy [5; 8; 23; 30; 31; 32], raise the number of commands [12; 33], increase the information transfer rate (ITR) [34; 35; 36; 37; 38], or reduce the training efforts [7; 30; 34; 39]. To enhance the prediction accuracy, new classification algorithms [30; 40; 41] or feature extraction methods have been proposed [31; 32; 42]. Recent BCI studies frequently applied deep learning algorithms for high accuracy [5; 23]. The BCI study using deep learning shows 99.38% prediction accuracy for motor imagery tasks [30]. Furthermore, various stimulus-presentation methods were suggested to increase the number of commands [12; 15; 43]. A recent study implemented a speller with 160 characters by combining different frequency signals [12]. Another critical issue in BCI fields is improving typing speed while maintaining high accuracy. Canonical Correlation Analysis (CCA) is often used in SSVEP spellers and shows good ITR performance [35; 36; 44]. A hybrid BCI study combining an eye tracker and SSVEP achieved considerably high ITR [37; 38]. The accuracy and ITR of the hybrid study were 95.2% and 360.7 ppm, respectively [38]. To reduce the time measuring training data, data augmentation or transfer learning approaches were often used [39]. The prediction model can be trained with fewer data by augmenting the data. The data augmentation could be achieved by cropping the signals with a sliding window [45], adding noise [46], or segmenting and recombining the signals [47]. Recent studies also used generative deep learning algorithms to create artificial neural signals as training data [48; 49]. An alternative approach to reducing acquisition time is transfer learning. It utilizes the pre-trained model for new subjects [50; 51]. Therefore, transfer learning requires less training data and reduces the acquisition time. Despite these BCI studies, there were still critical limitations for disabled people to using the BCI system in real life. Here, we introduced our BCI studies to overcome the barriers for practical use. We suggested the methods of estimating arm movement with high accuracy using a non-invasive method and compensating the prediction. Moreover, the study was described predicting the user's state and changing the system mode. A study predicting multiple intentions was also introduced. Our future study will develop a real-time multi-functional BCI system combining automatic prediction correction, mode change, and multi-intention prediction. In our opinion, automatic control and suggestion systems will be crucial to the BCIs for safe and efficient use of the BCI system in daily life. For instance, autonomous driving of an electric wheelchair by commanding the destination will be more convenient and safe than controlling the wheelchair constantly. Moreover, it will be convenient if the BCI system can suggest proper behavior based on circumstances, schedule, and time like an artificial intelligent secretary. The smart BCI may ask whether the user is hungry based on the user's routine and time, suggest and order foods according to the preference, and automatically feed the user. To develop smart BCIs, several state-of-the-art technologies such as autonomic driving, context recognition, robotics, and artificial intelligence should be combined. Although we proposed some approaches to overcome the challenges for the practical use, there are still many limitations when using BCIs in daily life, such as the inconvenience of electrode attachment, system recharge, and system detachment. Nevertheless, we hope that our studies will be a foundation for the development of a practical BCI system. Writing--original draft preparation, W-S.C.; writing--review and editing, H-G.Y. ; visualization, H-G.Y. All authors have read and agreed to the published version of the manuscript. This study was supported by research fund from Chosun University (2020). Not applicable. Not applicable. Not applicable.