Commercial Brain Computer Interface Research Articles

Electroencephalogram (EEG) Signal Classification Using Artificial Neural Network to Control Electric Artificial Hand Movement

All due to the complex nature of the electroencephalography (EEG) signal, it is a challenge to be able to use it as the driver of an electric artificial hand. By using EEG signal, the command for artificial hand movements becomes more intuitive and natural. This study aims to classify EEG signals to serve as electronic hand control. Classification is conducted using artificial neural networks (ANN), in which EEG signal datasets are obtained from a commercial brain computer interface (BCI). The ANN model obtained is expected to be able to determine that the EEG signal is one of the five EEG signals generated from five predetermined hand movements. This study proposes feature extraction and processing that is very simple but performs well, indicated by its small error value. The results show that ANN can classify five hand movements tested with an overall accuracy rate of 80%.

Caregiver and special education staff perspectives of a commercial brain-computer interface as access technology: a qualitative study

ABSTRACTThis study sought to understand the perceptions of special education staff and caregivers (n = 6) who took part in a brain-computer interface (BCI) technology trial for individuals with severe cerebral palsy. Participants were interviewed post-trials regarding the different BCI components. The transcripts were coded and analyzed using thematic analysis. Results showed that BCIs are not suitable for independent use outside of clinical/laboratory settings. The hardware needs to be configurable, comfortable and accommodate physical support needs. The training approach needs to be less cognitively demanding, motivating and support personalized mental tasks. For BCIs to transition into the real world, there should be adequate technological support, improved reliability, and a systemic assessment of how the technology will fit into the lives of end users. Participants emphasized the on-going need to involve users and individuals who support them, to create a system that truly meets the needs of the users.

An Online Synchronous Brain Wave Signal Pattern Classifier with Parallel Processing Optimization for Embedded System Implementation

Commercial Brain Computer Interface applications are currently expanding due to the success of widespread dis-semination of low cost devices. Reducing the cost of a traditional system requires appropriate resources, such as proper software tools for signal processing and characterization. In this paper, a methodology for classifying a set of attention and meditation brain wave signal patterns is presented by means of unsupervised signal feature clustering with batch Self-Organizing Maps (b-SOM) and supervised classification by Support Vector Machine (SVM). Previous research on this matter did not combine both methods and also required an important amount of computation time. With the use of a small square neuron grid by b-SOM and an RBF kernel SVM, a well delimited classifier was obtained. The recognition rate was 70% after parameter tuning. In terms of optimization, the parallel b-SOM algorithm reduced drastically the computation time, allowing online clustering and classification for full length input data.

Using a brain-computer interface (BCI) in reducing math anxiety: Evidence from South Africa

Prior studies have indicated that learning mathematics is highly associated with attitudes towards mathematics and emotions like math anxiety. Over the years, strong empirical evidence has emerged, showing that math anxiety has a significant negative effect on mathematics performance. Interestingly enough, some researchers have shown that math anxiety can be trained and reduced. However, the proposed interventions have mostly focused on teachers as oppose to students, while the existing physiological approaches like cognitive behavioural therapy require administration by trained professionals. With recent advancements in technology, low cost commercial brain-computer interface (BCI) devices that can capture human emotions in real time have been developed and can have a potential use in training and reducing math anxiety. In this study, the objective was to determine if using a BCI mathematics educational game can help students to effectively reduce math anxiety. To attain this objective, a within-subjects longitudinal research design with eight data gathering waves was adopted as the primary methodology to ascertain changes in the participant's level of math anxiety across two sessions that took place on separate days. Analysis of data captured across two training sessions with a BCI mathematics educational game showed that math anxiety can be effectively trained and reduced with a BCI. In addition, the results showed that math anxiety has a significant negative impact on mathematics performance which is congruent with prior studies. These findings provide a novel way in which a low cost non-invasive BCI device can be used for educational purposes.

BCI Promises in Emotional Involvement in Music and Games

The reliability of commercial non-invasive BCI (brain computer Interface) devices and the lower cost of these EEG-based systems, as well as the equipment's portability, determined the increasing interest in their application in different research fields. The latter feature makes BCI devices particularly suited for entertainment applications, especially given the possibility to detect the mental state of the users. The relationship between emotions and entertainment is obvious, as is the influence of music in human emotional states. While BCI devices represent a challenge in gaming motion control, they have been successfully applied in music production [Dan et al. 2009] and composition [Hamadicharef et al. 2010]. In our previous work [Conscious and unconscious music from the brain in press] we focused on conscious production of music notes with the aim of developing a prototype for applications in entertainment. In this work we trace the state-of-the art of our research and present our opinion on possible applications of the preliminary results.

Evaluating the ergonomics of BCI devices for research and experimentation

The use of brain computer interface (BCI) devices in research and applications has exploded in recent years. Applications such as lie detectors that use functional magnetic resonance imaging (fMRI) to video games controlled using electroencephalography (EEG) are currently in use. These developments, coupled with the emergence of inexpensive commercial BCI headsets, such as the Emotiv EPOC (http://emotiv.com/index.php) and the Neurosky MindWave, have also highlighted the need of performing basic ergonomics research since such devices have usability issues, such as comfort during prolonged use, and reduced performance for individuals with common physical attributes, such as long or coarse hair. This paper examines the feasibility of using consumer BCIs in scientific research. In particular, we compare user comfort, experiment preparation time, signal reliability and ease of use in light of individual differences among subjects for two commercially available hardware devices, the Emotiv EPOC and the Neurosky MindWave. Based on these results, we suggest some basic considerations for selecting a commercial BCI for research and experimentation. Statement of Relevance: Despite increased usage, few studies have examined the usability of commercial BCI hardware. This study assesses usability and experimentation factors of two commercial BCI models, for the purpose of creating basic guidelines for increased usability. Finding that more sensors can be less comfortable and accurate than devices with fewer sensors.

Impact of Spatial Filters During Sensor Selection in a Visual P300 Brain-Computer Interface

A challenge in designing a Brain-Computer Interface (BCI) is the choice of the channels, e.g. the most relevant sensors. Although a setup with many sensors can be more efficient for the detection of Event-Related Potential (ERP) like the P300, it is relevant to consider only a low number of sensors for a commercial or clinical BCI application. Indeed, a reduced number of sensors can naturally increase the user comfort by reducing the time required for the installation of the EEG (electroencephalogram) cap and can decrease the price of the device. In this study, the influence of spatial filtering during the process of sensor selection is addressed. Two of them maximize the Signal to Signal-plus-Noise Ratio (SSNR) for the different sensor subsets while the third one maximizes the differences between the averaged P300 waveform and the non P300 waveform. We show that the locations of the most relevant sensors subsets for the detection of the P300 are highly dependent on the use of spatial filtering. Applied on data from 20 healthy subjects, this study proves that subsets obtained where sensors are suppressed in relation to their individual SSNR are less efficient than when sensors are suppressed in relation to their contribution once the different selected sensors are combined for enhancing the signal. In other words, it highlights the difference between estimating the P300 projection on the scalp and evaluating the more efficient sensor subsets for a P300-BCI. Finally, this study explores the issue of channel commonality across subjects. The results support the conclusion that spatial filters during the sensor selection procedure allow selecting better sensors for a visual P300 Brain-Computer Interface.

Comparison Study of Biosignal based Computer Interfaces Based on Fitts’ Law Paradigm

Abstract This paper presents development of two different biosignal based computer interfaces for people with motor disabilities: 1) electromyography (EMG)-based computer interface (ECI) and 2) hybrid EMG-based computer interface (HECI). The ECI made both movements of a cursor and clicking from muscle contractions induced by volitional wrist movements. On the other hand, the HECI made movements of a cursor from the lower arm movements under a motion capture camera, and clicking from muscle contractions induced by volitional movements of index and middle fingers. These interfaces were tested by the experiments based on a Fitts’ law paradigm in order to provide object evaluation of the interfaces. These results were also compared to a commercial brain computer interface was evaluated under the same test setup.