# Intelligent Control of Robotic Arm Using Brain Computer Interface and Artificial Intelligence
###### Abstract  
The combination of signal processing and Artificial Intelligence (AI) is revolutionizing the robotics and automation industry by the deployment of intelligent systems and reducing human intervention. Reading human brain signal through electroencephalography (EEG) has provided a new direction of research that automate machines through the human brain and computer interface or Brain-Computer Interface (BCI). The study is also inspired by the same concept of intelligently controlling a robotic arm using BCI and AI to help physically disabled individuals. The proposed system is non-invasive, unlike existing technologies that provide a reliable comparison of different AI-based classification algorithms. This paper also predicts a reliable bandwidth for the BCI process and provides exact placements of EEG electrodes to verify different arm moments. We have applied different classification algorithms, i.e., Random Forest, KNN, Gradient Boosting, Logistic Regression, SVM, and Decision Tree, to four different users. The accuracy of all prescribed classifiers has been calculated by considering the first user as a reference. The presented results validate the novel deployment, and the comparison shows that the accuracy for Random Forest remained optimal at around 76%, Gradient Boosting is around 74%, while the lowest is 64% for Decision Tree. It has been observed that people have different activation bandwidths while the dominant frequency varies from person-to-person that causes fluctuations in the EEG dataset. electroencephalogram (EEG); brain-computer interface (BCI); artificial intelligence (AI); classification; feature-extraction; microcontroller; mind-controlled robots; Python +
Footnote †: journal: Medical and Computer Engineering  
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Footnote 1: email: _Applied sciences_  
Footnote †: journal: Medical and Computer Engineering  
BCI can replace useful functions for disabled people suffering from neuromuscular disorders. A BCI is a computer-based device that captures an EEG and converts them into commands that are sent to an output device to perform the desired action [4]. In contrast to all other devices, it is completely non-invasive [5], cost-effective, and unique in that it can be utilized to address a wide array of needs, including those associated with prosthetics. In order to make the signal useful, its desired information is converted into active functions. It is the active function that operates the microcontroller [6; 7]. Based on the required input, the microcontroller determines the degree of freedom of the actuators, and the robotic arm acts as per the received signal. Moreover, the electric impulses transmit between neurons in the brain during sleep which is measured by EEG. The recorded EEG signals are obtained through a single electrode that uses a Local field potential (LFP) to measure EEG signals [7]. LFPs are temporary electrical signals generated by individual neurons in the brain and other tissues when their electrical activity is summed and synchronized [7]. The placement of electrodes is critical to this design. Typically, electrodes consisting of conductors such as gold or silver chloride are utilized. A headset is used to translate EEG data into movement patterns that measure brain activity via the scalp of the head. AF3, F7, F3, F55, P7, O1, O2, P8, T8, FC6, F4, F8, and AF4 electrodes are calibrated on this 14-channel hardware. Odd-numbered electrodes are located on the left hemisphere, while even-numbered electrodes are located on the right [8]. Therefore, the maximum number of degrees of freedom (DOF) is identified [9]. To implement the code, we have used a jupyter notebook. It is an Integrated Development Environment (IDE) for Python. The classification is performed using machine learning and artificial intelligence techniques. Machine learning aims to understand the structure of data and make models based on that data that are human-comprehensible and useful.