By evaluating the OpenBCI Ultracortex Mark IV EEG headset's affordability, accessibility, and accuracy, it can be compared to other commercial- and medical-grade EEGs that have been evaluated previously. Nine participants used the Ultracortex Mark IV for the P300 Speller classification task, with the accuracy being measured along with the time required to set up the Mark IV, the participants' comfort, and the participants' perceived ease of setup. These results were then compared to previous evaluations of other EEG headsets. The OpenBCI Ultracortex Mark IV's cost-effectiveness ratio is relatively low compared to the other EEG systems, and its ease of use and speed of set up are notably better than the other headsets being compared.

With the ever-progressing development in the field of computational and analytical science the last decade has seen a big improvement in the accuracy of electroencephalography (EEG) technology. Studies try to examine possibilities to use high dimensional EEG data as a source for Brain to Computer Interface. Applications of EEG Brain to computer interface vary from emotion recognition, simple computer/device control, speech recognition up to Intelligent Prosthesis. Our research presented in this paper was focused on the study of the problematic speech activity detection using EEG data. The novel approach used in this research involved the use visual stimuli, such as reading and colour naming, and signals of speech activity detectable by EEG technology. Our proposed solution is based on a shallow Feed-Forward Artificial Neural Network with only 100 hidden neurons. Standard features such as signal energy, standard deviation, RMS, skewness, kurtosis were calculated from the original signal from 16 EEG electrodes. The novel approach in the field of Brain to computer interface applications was utilised to calculated additional set of features from the minimum phase signal. Our experimental results demonstrated F1 score of 86.80% and 83.69% speech detection accuracy based on the analysis of EEG signal from single subject and cross-subject models respectively. The importance of these results lies in the novel utilisation of the mobile device to record the nerve signals which can serve as the stepping stone for the transfer of Brain to computer interface technology from technology from a controlled environment to the real-life conditions.

Brain-computer interface (BCI) technologies connect brain sensing devices to computers where these signals are analyzed and processed. This makes the connection between the central nervous system and the computer. The most widely used BCI technologies include electroencephalograph. Electroencephalography brings the possibility of a mobile BCI system usable in life. However, the creation of such a mass market system still requires solving many fundamental problems. Automatic speech recognition gains huge improvements in recent years. Deep neural networks used in speech recognition chain improved speech recognition accuracy to very high levels. Also these days, end-to-end speech recognizers are getting better. In contrast with these recent improvements, end-user automatic speech recognition acceptance is quite low. It is due to the fact that it does not work very well on long distances from a microphone, yet. It is also impossible to use speech recognizers in places such as open offices or public places due to background noise. Another problem is that people do not want to disclose private or confidential information on loud. Electroencephalography based imagine speech recognizers could solve this acceptance rate. Speech recognizers may supplement speech recognizes from the microphone in situations where background noise is very high. Speech activity detector is a necessary component in the Speech recognition chain and it is also true in EEG based speech recognition. Methods for speech activity detection from EEG signals are proposed in this paper.

Automatic speech recognition gain huge improvements in recent years. Deep neural networks used in speech recognition chain improved speech recognition accuracy to very high levels. Also these days, end-to-end speech recognizers are getting better. In contrast with these recent improvements, end user automatic speech recognition acceptance is quite low. It is due to fact that it does not work very well on long distances from microphone, yet. It is also impossible to use speech recognizers in places such as open offices or public places due to background noise. Another problem is that people do not want to disclose private or confidential information on loud. EEG based imagine speech recognizers could solve this acceptance rate. Overt speech recognizers may supplement speech recognizes from the microphone in situations where background noise is very high. Voice activity detector is a necessary component in Speech recognition chain and it is also true in EEG based speech recognition. Methods for voice activity detection from EEG signals are proposed in this paper.

This article is an overview of Brain - Computer Interface technology and speech recognition based on brain waves. Its main purpose is to point out current state-of-the-art technology, to focus on global research and to continue the work in BCI technology. The aim is a thorough understanding of current direct speech recognition from brain signals. The first author is working on contributing to BCI research throughout my doctoral studies.

In this paper, research focused on speech activity detection using brain EEG signals is presented. In addition to speech stimulation of brain activity, an innovative approach based on the simultaneous stimulation of the brain by visual stimuli such as reading and color naming has been used. Designing the solution, classification using two types of artificial neural networks were proposed: shallow Feed-forward Neural Network and deep Convolutional Neural Network. Experimental results of classification demonstrated F1 score 79.50% speech detection using shallow neural network and 84.39% speech detection using deep neural network based on cross-evaluated classification models.