# Intelligent Control of Robotic Arm Using Brain Computer Interface and Artificial Intelligence
###### Abstract
Figure 1 illustrates an overview of the proposed system that shows an extremely versatile robotic arm controlled by reading brain signals. Degrees of freedom is the most important aspect of this brain-controlled arm that corresponds to the joint of each other that represents the point on the arm where the arm can bend, rotate or translate. A joint can perform two motions: rotation and translation. A link is a connector that connects two actuators. This BCI-based study shows how to actuate a robotic arm with the help of device commands derived from EEG signals. We have used an emotive headset to acquire EEG signals from the brain, which are further used to run the actuator of the robotic arm through artificial intelligence and machine learning techniques. In this area, some work has also been conducted that proposed different methods to control different objects. We have discussed the most specific works, i.e., the author in [10] has proposed an attention mechanism for the classification of motor imagery (MI) signals. As a result of the dynamic properties of the motor imagery signals, the attention  
Figure 1: Block Diagram and System Architecture. mechanism can extract spatial context features. This model is more accurate than current best practices that use convolution with varying filter sizes to extract features from MI signals in various spatial contexts. The authors in [11] have used an inception approach that claims accuracy with the BCI challenge dataset. Further, a variety of deep learning methods have been developed in the existing literature to classify motor imagery (MI) [8; 9; 10; 11; 12]. In [13], CNN is used which uses spatial features to classify EEG signals which also shows relatively better results. In [14; 15; 16; 17], EEG data has been directly fed into the long short-term memory (LSTM) models. The authors in [18] developed an application of BCI for LabVIEW with which a robotic hand is controlled by voluntary blinking of the eyes. The embedded sensor in the Neurosky MindWave Mobile headset picked up the blinking. Another study proposed the design of a wheelchair controlled by an EEG signal [19]. The authors have constructed an electric wheelchair that can be directly controlled by the brain of the disabled person. Emotiv EPOC headset has been used for the acquisition of EEG signals. Additionally, an Arduino IDE is used for the programming. Their program can read and process the EEG data from the Emotiv EPOC headset into mental command. In [20], the authors used a headset but did not use any classification algorithms for the classification of signals and nor achieve an accuracy of more than 60%. The criteria of the total number of users are also not achieved in this proposal. In [21; 22; 23; 24; 25; 26; 27; 28; 29], the authors have implemented a better version of the BCI process as they used headset and machine learning algorithms in Python. Consequently, their proposals are also validated through their results that achieved an accuracy of more than 60%, and the product is also used by most \(\text{r}_{\text{s}}\). In [30; 31; 32; 33; 34; 35], the authors used the headset for signal acquisition, and reunetworks and fuzzy logic have been implemented to achieve an accuracy of more than 60%, and the product was used by more than two users. However, like [36], no classification algorithms are employed, nor any framework for programming is used. In the proposed work, we have used the Emotive EPOC headset for signals acquisition to predict a reliable bandwidth for the BCI process and provide 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 proposal is validated through results that achieve an accuracy of more than 60%. Table 1 shows the comparison between different related studies and proposed work.