This paper presents a systematic literature review on Brain-Computer Interfaces (BCIs) in the context of Machine Learning. Our focus is on Electroencephalography (EEG) research, highlighting the latest trends as of 2023. The objective is to provide undergraduate researchers with an accessible overview of the BCI field, covering tasks, algorithms, and datasets. By synthesizing recent findings, our aim is to offer a fundamental understanding of BCI research, identifying promising avenues for future investigations.

Abstract: In this study, we illustrate the progress of BCI research and present scores of unveiled contemporary approaches. First, we explore a decoding natural speech approach that is designed to decode human speech directly from the human brain onto a digital screen introduced by Facebook Reality Lab and University of California San Francisco. Then, we study a recently presented visionary project to control the human brain using Brain-Machine Interfaces (BMI) approach. We also investigate well-known electroencephalography (EEG) based Emotiv Epoc Neuroheadset to identify six emotional parameters including engagement, excitement, focus, stress, relaxation, and interest using brain signals by experimenting the neuroheadset among three human subjects where we utilize two supervised learning classifiers, Naive Bayes and Linear Regression to show the accuracy and competency of the Epoc device and its associated applications in neurotechnological research. We present experimental studies and the demonstration indicates 69% and 62% improved accuracy for the aforementioned classifiers respectively in reading the performance matrices of the participants.

Scientists at the Army Research Laboratory (ARL) are covering some new ground in artificial intelligence (AI) by connecting a machine with human intelligence via a neural connection. Not to worry: the research team isn't cooking up an AI system that will run the show inside a person's head. But it does have promise for both medical as well as deep machine learning systems uses, potentially in military and everyday applications. The work by ARL and the DCS Corporation recently won the Neurally Augmented Image Labelling Strategies, or NAILS, challenge at an international machine learning research competition in Tokyo, according to ARL. The goal of NAILS was to incorporate brain activity into machine learning methods that can detect if an image a person was seeing was relevant to the task at hand, as part of an effort to improve the ability of humans and machines to manage information by working together.