**5. Challenges and future direction**

Finding the most effective technique for features extraction and selection has been a challenge as each technique has its own advantages and disadvantages. Besides, EEG itself is also highly non-linear and artifact-prone. Together, a low classification accuracy may result. Combining different classifiers or biosignals can improve this accuracy, but the training time to master the control is much prolonged which in turn affects the overall efficiency. Future studies using subjects with pathological disorders instead of healthy ones are encouraged so to increase the generalizability in the biomedical field. In terms of non-biomedical applications such as art, gaming and entertainment, this is a potential market that contributes to economic growth. However, developing a "dependable system with stable performance with different mental states" that can adapt to different environments is the main goal to gain its public acceptance in the next decade (**Table 1**) [4].


#### **Table 1.**

*Comparative analysis of various methods used for recording features.*
