The Utility of Electroencephalography for User Input

The goal of this paper is to introduce the use of noninvasive brain-computer interfaces to prospective computer scientists. Electroencephalography is explained starting with how a user’s brain waves are measured and ending with how the data is parsed to software programs. To further expand on the ability to implement electroencephalography into software code, and example of a simple game is given. This game is an endless runner, meaning that it has no end and stops once the player’s game piece collides with an object. It is coded in the Python computer language

Electromagnetic signals in noninvasive brain-computer interfaces

A brain-computer interface (BCI) translates task-related brain activity into computer commands. Detecting this activity is difficult, as the measured brain signals are generated by multiple sources and also include task-irrelevant brain activity. Using conventional methods such as signal averaging is not possible, because subjects should receive online feedback of their performance. BCI users usually either learn to control some components of their brain activity with the help of feedback or are presented with some stimuli that produce detectable signals in the brain. This thesis reviews BCI research, basic principles of electroencephalography (EEG) and magnetoencephalography (MEG), the sensorimotor cortex, and then describes experimental BCI studies. The thesis comprises of five publications studying 1) sensorimotor cortical activation for BCI, 2) use of MEG for BCIs, 3) single brain signal trials during (attempted) finger movements for online BCI classification and 4) vibrotactile feedback in comparison to visual feedback. Participants were 45 healthy, 9 tetraplegic, and 3 paraplegic subjects. First our results of tetraplegic subjects show that their 10- and 20-Hz rhythmic activity is more widespread and less contralateral than that of healthy subjects, providing a poorer control signal for two-class movement classification. For separating brain signals during right and left attempted movement, we selected features from the low-frequency bands. Second, our results show that for classification, MEG is not superior to EEG for two-class BCI, despite being a more localised measurement technique. Third, brain signals during finger movements could be classified online with high accuracy after basically no training. However, results from the tetraplegic subjects are much worse than those of the healthy subjects. Fourth, we show that vibrotactile feedback can be used as an alternative feedback channel during training and is especially useful when visual attention is needed for application control. On the basis of these and earlier findings, it is concluded that accurate control of noninvasive BCI is possible but requires some training. Future important research involves more work with motor-disabled patients, especially when testing new signal processing methods. Better performance may also be achieved using different feedback modalities

Mental state estimation for brain-computer interfaces

Mental state estimation is potentially useful for the development of asynchronous brain-computer interfaces. In this study, four mental states have been identified and decoded from the electrocorticograms (ECoGs) of six epileptic patients, engaged in a memory reach task. A novel signal analysis technique has been applied to high-dimensional, statistically sparse ECoGs recorded by a large number of electrodes. The strength of the proposed technique lies in its ability to jointly extract spatial and temporal patterns, responsible for encoding mental state differences. As such, the technique offers a systematic way of analyzing the spatiotemporal aspects of brain information processing and may be applicable to a wide range of spatiotemporal neurophysiological signals

Recent and upcoming BCI progress: overview, analysis, and recommendations

Brain–computer interfaces (BCIs) are finally moving out of the laboratory and beginning to gain acceptance in real-world situations. As BCIs gain attention with broader groups of users, including persons with different disabilities and healthy users, numerous practical questions gain importance. What are the most practical ways to detect and analyze brain activity in field settings? Which devices and applications are most useful for different people? How can we make BCIs more natural and sensitive, and how can BCI technologies improve usability? What are some general trends and issues, such as combining different BCIs or assessing and comparing performance? This book chapter provides an overview of the different sections of this book, providing a summary of how authors address these and other questions. We also present some predictions and recommendations that ensue from our experience from discussing these and other issues with our authors and other researchers and developers within the BCI community. We conclude that, although some directions are hard to predict, the field is definitely growing and changing rapidly, and will continue doing so in the next several years

Control of the electric wheelchair using EEG classification

Electric wheelchairs are some of the most important devices to assist physically handicapped persons. This paper presents the concept of brain controlled electric wheelchair designed for people who are not able to use other interfaces such as a hand joystick, and in particular for patients suffering from amyotrophic lateral sclerosis (ALS). The objective is to control the direction of an electric wheelchair using noninvasive scalp electroencephalogram (EEG)

Integrating EEG and MEG signals to improve motor imagery classification in brain-computer interfaces

We propose a fusion approach that combines features from simultaneously recorded electroencephalographic (EEG) and magnetoencephalographic (MEG) signals to improve classification performances in motor imagery-based brain-computer interfaces (BCIs). We applied our approach to a group of 15 healthy subjects and found a significant classification performance enhancement as compared to standard single-modality approaches in the alpha and beta bands. Taken together, our findings demonstrate the advantage of considering multimodal approaches as complementary tools for improving the impact of non-invasive BCIs

Using Noninvasive Brain Measurement to Explore the Psychological Effects of Computer Malfunctions on Users during Human-Computer Interactions

In today’s technologically driven world, there is a need to better understand the ways that common computer malfunctions affect computer users. These malfunctions may have measurable influences on computer user’s cognitive, emotional, and behavioral responses. An experiment was conducted where participants conducted a series of web search tasks while wearing functional nearinfrared spectroscopy (fNIRS) and galvanic skin response sensors. Two computer malfunctions were introduced during the sessions which had the potential to influence correlates of user trust and suspicion. Surveys were given after each session to measure user’s perceived emotional state, cognitive load, and perceived trust. Results suggest that fNIRS can be used to measure the different cognitive and emotional responses associated with computer malfunctions. These cognitive and emotional changes were correlated with users’ self-report levels of suspicion and trust, and they in turn suggest future work that further explores the capability of fNIRS for the measurement of user experience during human-computer interactions