A survey in implementation and applications of electroencephalograph (EEG)‎-based brain-computer interface

الملخص EN

A Brain-Computer Interface (BCI) is an external system that controls activities and processes in the physical world based on brain signals.

In Passive BCI, artificial signals are automatically generated by a computer program without any input from nerves in the body.

This is useful for individuals with mobility issues.

Traditional BCI has been dependent only on recording brain signals with Electroencephalograph (EEG) and has used a rule-based translation algorithm to generate control commands.

These systems have developed very accurate translation systems.

This paper is about the different methods for adapting the signals from the brain.

It has been mentioned that various kinds of surveys in the past to serve the purpose of the present research.

This paper shows a simple and easy analysis of each technique and its respective benefits and drawbacks, including signal acquisition, signal pre-processing, feature classification and classification.

Finally, discussed is the application of EEG-based is an external system that controls activities and processes in the physical world based on brain signals.

In Passive BCI, artificial signals are automatically generated by a computer program without any input from nerves in the body.

This is useful for individuals with mobility issues.

Traditional BCI has been dependent only on recording brain signals with Electroencephalograph (EEG) and has used a rule-based translation algorithm to generate control commands.

These systems have developed very accurate translation systems.

This paper is about the different methods for adapting the signals from the brain.

It has been mentioned that various kinds of surveys in the past to serve the purpose of the present research.

This paper shows a simple and easy analysis of each technique and its respective benefits and drawbacks, including signal acquisition, signal pre-processing, feature classification and classification.

Finally, discussed is the application of EEG-based BCI.