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. We envision that non-invasive, insertable, and low-cost BCI
approaches shall be the focal point for not only an alternative for patients
with physical paralysis but also understanding the brain that would pave us to
access and control the memories and brain somewhere very near.

本文介绍了脑机接口（BCI）的最新研究，并探讨了通过 Facebook Reality Lab 和加州大学旧金山分校的直接将人类语音从人脑解码为数字屏幕输出的方法，以及以 BMI 为基础的控制人脑的远景项目。通过实验 Emotiv Epoc+ Neuroheadset，利用两个监督学习分类器，朴素贝叶斯和线性回归，识别情绪参数，研究表明该设备及其关联应用在神经技术研究中的准确性与能力。非侵入性，可植入和低成本的脑机接口方法不仅是肢体瘫痪患者的替代品，还有助于理解大脑，为我们开启接近记忆和大脑控制的大门。