Non-invasive Brain-computer Interface Research Articles

Usability of non-invasive brain computer interface systems for upper limb recovery after stroke

Usability of non-invasive brain computer interface systems for upper limb recovery after stroke

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.

Predicting BCI Subject Performance Using Probabilistic Spatio-Temporal Filters

Recently, spatio-temporal filtering to enhance decoding for Brain-Computer-Interfacing (BCI) has become increasingly popular. In this work, we discuss a novel, fully Bayesian–and thereby probabilistic–framework, called Bayesian Spatio-Spectral Filter Optimization (BSSFO) and apply it to a large data set of 80 non-invasive EEG-based BCI experiments. Across the full frequency range, the BSSFO framework allows to analyze which spatio-spectral parameters are common and which ones differ across the subject population. As expected, large variability of brain rhythms is observed between subjects. We have clustered subjects according to similarities in their corresponding spectral characteristics from the BSSFO model, which is found to reflect their BCI performances well. In BCI, a considerable percentage of subjects is unable to use a BCI for communication, due to their missing ability to modulate their brain rhythms–a phenomenon sometimes denoted as BCI-illiteracy or inability. Predicting individual subjects’ performance preceding the actual, time-consuming BCI-experiment enhances the usage of BCIs, e.g., by detecting users with BCI inability. This work additionally contributes by using the novel BSSFO method to predict the BCI-performance using only 2 minutes and 3 channels of resting-state EEG data recorded before the actual BCI-experiment. Specifically, by grouping the individual frequency characteristics we have nicely classified them into the subject ‘prototypes’ (like μ - or β -rhythm type subjects) or users without ability to communicate with a BCI, and then by further building a linear regression model based on the grouping we could predict subjects' performance with the maximum correlation coefficient of 0.581 with the performance later seen in the actual BCI session.

Brain-Computer Interface

As the power of modern computers grows alongside our understanding of the human brain, we move ever closer to making some pretty spectacular science fiction into reality. Imagine transmitting signals directly to someone's brain that would allow them to see, hear or feel specific sensory inputs. Consider the potential to manipulate computers or machinery with nothing more than a thought. It isn't about convenience, for severely disabled people, development of a brain-computer interface (BCI) could be the most important technological breakthrough in decades. A Brain-computer interface, sometimes called a direct neural interface or a brain-machine interface, is a direct communication pathway between a brain and an external device. It is the ultimate in development of human-computer interfaces or HCI. BCIs being the recent development in HCI there are many realms to be explored. After experimentation three types of BCIs have been developed namely Invasive BCIs, Partially-invasive BCIs, Non-invasive BCIs.

Decoding individual finger movements from one hand using human EEG signals.

Brain computer interface (BCI) is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimensions from decoding movements of, mainly, large body parts, e.g., upper and lower limbs. It has been reported that complicated dexterous functions, i.e., finger movements, can be decoded in electrocorticography (ECoG) signals, while it remains unclear whether noninvasive electroencephalography (EEG) signals also have sufficient information to decode the same type of movements. Phenomena of broadband power increase and low-frequency-band power decrease were observed in EEG in the present study, when EEG power spectra were decomposed by a principal component analysis (PCA). These movement-related spectral structures and their changes caused by finger movements in EEG are consistent with observations in previous ECoG study, as well as the results from ECoG data in the present study. The average decoding accuracy of 77.11% over all subjects was obtained in classifying each pair of fingers from one hand using movement-related spectral changes as features to be decoded using a support vector machine (SVM) classifier. The average decoding accuracy in three epilepsy patients using ECoG data was 91.28% with the similarly obtained features and same classifier. Both decoding accuracies of EEG and ECoG are significantly higher than the empirical guessing level (51.26%) in all subjects (p<0.05). The present study suggests the similar movement-related spectral changes in EEG as in ECoG, and demonstrates the feasibility of discriminating finger movements from one hand using EEG. These findings are promising to facilitate the development of BCIs with rich control signals using noninvasive technologies.

Brain–Computer Interface With Language Model–Electroencephalography Fusion for Locked-In Syndrome

Background. Some noninvasive brain–computer interface (BCI) systems are currently available for locked-in syndrome (LIS) but none have incorporated a statistical language model during text generation. Objective. To begin to address the communication needs of individuals with LIS using a noninvasive BCI that involves rapid serial visual presentation (RSVP) of symbols and a unique classifier with electroencephalography (EEG) and language model fusion. Methods. The RSVP Keyboard was developed with several unique features. Individual letters are presented at 2.5 per second. Computer classification of letters as targets or nontargets based on EEG is performed using machine learning that incorporates a language model for letter prediction via Bayesian fusion enabling targets to be presented only 1 to 4 times. Nine participants with LIS and 9 healthy controls were enrolled. After screening, subjects first calibrated the system, and then completed a series of balanced word generation mastery tasks that were designed with 5 incremental levels of difficulty, which increased by selecting phrases for which the utility of the language model decreased naturally. Results. Six participants with LIS and 9 controls completed the experiment. All LIS participants successfully mastered spelling at level 1 and one subject achieved level 5. Six of 9 control participants achieved level 5. Conclusions. Individuals who have incomplete LIS may benefit from an EEG-based BCI system, which relies on EEG classification and a statistical language model. Steps to further improve the system are discussed.

A P300-based brain-computer interface with stimuli on moving objects: four-session single-trial and triple-trial tests with a game-like task design.

Brain-computer interfaces (BCIs) are tools for controlling computers and other devices without using muscular activity, employing user-controlled variations in signals recorded from the user’s brain. One of the most efficient noninvasive BCIs is based on the P300 wave of the brain’s response to stimuli and is therefore referred to as the P300 BCI. Many modifications of this BCI have been proposed to further improve the BCI’s characteristics or to better adapt the BCI to various applications. However, in the original P300 BCI and in all of its modifications, the spatial positions of stimuli were fixed relative to each other, which can impose constraints on designing applications controlled by this BCI. We designed and tested a P300 BCI with stimuli presented on objects that were freely moving on a screen at a speed of 5.4°/s. Healthy participants practiced a game-like task with this BCI in either single-trial or triple-trial mode within four sessions. At each step, the participants were required to select one of nine moving objects. The mean online accuracy of BCI-based selection was 81% in the triple-trial mode and 65% in the single-trial mode. A relatively high P300 amplitude was observed in response to targets in most participants. Self-rated interest in the task was high and stable over the four sessions (the medians in the 1st/4th sessions were 79/84% and 76/71% in the groups practicing in the single-trial and triple-trial modes, respectively). We conclude that the movement of stimulus positions relative to each other may not prevent the efficient use of the P300 BCI by people controlling their gaze, e.g., in robotic devices and in video games.

Probabilistic co-adaptive brain–computer interfacing

Objective. Brain–computer interfaces (BCIs) are confronted with two fundamental challenges: (a) the uncertainty associated with decoding noisy brain signals, and (b) the need for co-adaptation between the brain and the interface so as to cooperatively achieve a common goal in a task. We seek to mitigate these challenges. Approach. We introduce a new approach to brain–computer interfacing based on partially observable Markov decision processes (POMDPs). POMDPs provide a principled approach to handling uncertainty and achieving co-adaptation in the following manner: (1) Bayesian inference is used to compute posterior probability distributions (‘beliefs’) over brain and environment state, and (2) actions are selected based on entire belief distributions in order to maximize total expected reward; by employing methods from reinforcement learning, the POMDP’s reward function can be updated over time to allow for co-adaptive behaviour. Main results. We illustrate our approach using a simple non-invasive BCI which optimizes the speed–accuracy trade-off for individual subjects based on the signal-to-noise characteristics of their brain signals. We additionally demonstrate that the POMDP BCI can automatically detect changes in the user’s control strategy and can co-adaptively switch control strategies on-the-fly to maximize expected reward. Significance. Our results suggest that the framework of POMDPs offers a promising approach for designing BCIs that can handle uncertainty in neural signals and co-adapt with the user on an ongoing basis. The fact that the POMDP BCI maintains a probability distribution over the user’s brain state allows a much more powerful form of decision making than traditional BCI approaches, which have typically been based on the output of classifiers or regression techniques. Furthermore, the co-adaptation of the system allows the BCI to make online improvements to its behaviour, adjusting itself automatically to the user’s changing circumstances.

A Survey of Interactive Systems based on Brain-Computer Interfaces

Brain-Computer Interface (BCI) enables users to interact with a computer only through their brain biological signals, without the need to use muscles. BCI is an emerging research area but it is still relatively immature. However, it is important to reflect on the different aspects of the Human-Computer Interaction (HCI) area related to BCIs, considering that BCIs will be part of interactive systems in the near future. BCIs most attend not only to handicapped users, but also healthy ones, improving interaction for end-users. Virtual Reality (VR) is also an important part of interactive systems, and combined with BCI could greatly enhance user interactions, improving the user experience by using brain signals as input with immersive environments as output. This paper addresses only noninvasive BCIs, since this kind of capture is the only one to not present risk to human health. As contributions of this work we highlight the survey of interactive systems based on BCIs focusing on HCI and VR applications, and a discussion on challenges and future of this subject matter.

Ethical Challenges Associated with the Development and Deployment of Brain Computer Interface Technology

Brain Computer Interface (BCI) technology offers potential for human augmentation in areas ranging from communication to home automation, leisure and gaming. This paper addresses ethical challenges associated with the wider scale deployment of BCI as an assistive technology by documenting issues associated with the development of non-invasive BCI technology. Laboratory testing is normally carried out with volunteers but further testing with subjects, who may be in vulnerable groups is often needed to improve system operation. BCI development is technically complex, sometimes requiring lengthy recording sessions to achieve the necessary personalisation of the paradigms, and this can present ethical challenges that vary depending on the subject group. The paper contributes to the on-going ethical discussion surrounding the deployment BCI outside the specialist laboratory and suggests some tentative guidelines for BCI research teams, appropriate to those deploying the technology, derived from experience on a multisite project. Any tension between deployment and technical progress must be managed by a formal process within a multidisciplinary consortium.

Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface

Objective. At the balanced intersection of human and machine adaptation is found the optimally functioning brain–computer interface (BCI). In this study, we report a novel experiment of BCI controlling a robotic quadcopter in three-dimensional (3D) physical space using noninvasive scalp electroencephalogram (EEG) in human subjects. We then quantify the performance of this system using metrics suitable for asynchronous BCI. Lastly, we examine the impact that the operation of a real world device has on subjects' control in comparison to a 2D virtual cursor task. Approach. Five human subjects were trained to modulate their sensorimotor rhythms to control an AR Drone navigating a 3D physical space. Visual feedback was provided via a forward facing camera on the hull of the drone. Main results. Individual subjects were able to accurately acquire up to 90.5% of all valid targets presented while travelling at an average straight-line speed of 0.69 m s−1. Significance. Freely exploring and interacting with the world around us is a crucial element of autonomy that is lost in the context of neurodegenerative disease. Brain–computer interfaces are systems that aim to restore or enhance a user's ability to interact with the environment via a computer and through the use of only thought. We demonstrate for the first time the ability to control a flying robot in 3D physical space using noninvasive scalp recorded EEG in humans. Our work indicates the potential of noninvasive EEG-based BCI systems for accomplish complex control in 3D physical space. The present study may serve as a framework for the investigation of multidimensional noninvasive BCI control in a physical environment using telepresence robotics.

Psychosocial and Ethical Aspects in Non-Invasive EEG-Based BCI Research—A Survey Among BCI Users and BCI Professionals

In this paper, the results of a pilot interview study with 19 subjects participating in an EEG-based non-invasive brain–computer interface (BCI) research study on stroke rehabilitation and assistive technology and of a survey among 17 BCI professionals are presented and discussed in the light of ethical, legal, and social issues in research with human subjects. Most of the users were content with study participation and felt well informed. Negative aspects reported include the long and cumbersome preparation procedure, discomfort with the cap and the wet electrodes, problems concerning BCI control, and strains during the training sessions. In addition, some users reflected on issues concerning system security. When asked for morally problematic issues in this field of non-invasive BCI research, the BCI professionals stressed the need for correct information transfer, the obligation to avoid unrealistic expectations in study participants, the selection of study participants, benefits and strains of participation, BCI illiteracy, the possibility of detrimental brain modifications induced by BCI use, and problems that may arise at the end of the trials. Furthermore, privacy issues were raised. Based on the results obtained, psychosocial and ethical aspects of EEG-based non-invasive BCI research are discussed and possible implications for future research addressed.

On the use of Non-Invasive Brain-Computer Interface Technology in Neurorehabilitation

On the use of Non-Invasive Brain-Computer Interface Technology in Neurorehabilitation

Evaluation of EEG Features in Decoding Individual Finger Movements from One Hand

With the advancements in modern signal processing techniques, the field of brain-computer interface (BCI) is progressing fast towards noninvasiveness. One challenge still impeding these developments is the limited number of features, especially movement-related features, available to generate control signals for noninvasive BCIs. A few recent studies investigated several movement-related features, such as spectral features in electrocorticography (ECoG) data obtained through a spectral principal component analysis (PCA) and direct use of EEG temporal data, and demonstrated the decoding of individual fingers. The present paper evaluated multiple movement-related features under the same task, that is, discriminating individual fingers from one hand using noninvasive EEG. The present results demonstrate the existence of a broadband feature in EEG to discriminate individual fingers, which has only been identified previously in ECoG. It further shows that multiple spectral features obtained from the spectral PCA yield an average decoding accuracy of 45.2%, which is significantly higher than the guess level (P < 0.05) and other features investigated (P < 0.05), including EEG spectral power changes in alpha and beta bands and EEG temporal data. The decoding of individual fingers using noninvasive EEG is promising to improve number of features for control, which can facilitate the development of noninvasive BCI applications with rich complexity.

The cost of space independence in P300-BCI spellers

BackgroundThough non-invasive EEG-based Brain Computer Interfaces (BCI) have been researched extensively over the last two decades, most designs require control of spatial attention and/or gaze on the part of the user.MethodsIn healthy adults, we compared the offline performance of a space-independent P300-based BCI for spelling words using Rapid Serial Visual Presentation (RSVP), to the well-known space-dependent Matrix P300 speller.ResultsEEG classifiability with the RSVP speller was as good as with the Matrix speller. While the Matrix speller’s performance was significantly reliant on early, gaze-dependent Visual Evoked Potentials (VEPs), the RSVP speller depended only on the space-independent P300b. However, there was a cost to true spatial independence: the RSVP speller was less efficient in terms of spelling speed.ConclusionsThe advantage of space independence in the RSVP speller was concomitant with a marked reduction in spelling efficiency. Nevertheless, with key improvements to the RSVP design, truly space-independent BCIs could approach efficiencies on par with the Matrix speller. With sufficiently high letter spelling rates fused with predictive language modelling, they would be viable for potential applications with patients unable to direct overt visual gaze or covert attentional focus.

2A2-P18 BCIに用いる頭皮上脳波の検討(非接触センシング)

Objective of this study is development of a BCI (Brain Computer interface) system using noninvasive EEG (Electroencephalogram) that is measured from the scalp. In this paper, We described about an our developed EEG measurement system, measured EEG of holding right hand by using this system and analyzed in order to find the features. From results of experiments, I think that our system will be useful for the study of developing noninvasive BCI system.

Electroencephalography Signal Grouping and Feature Classification Using Harmony Search for BCI

This paper presents a heuristic method for electroencephalography (EEG) grouping and feature classification using harmony search (HS) for improving the accuracy of the brain-computer interface (BCI) system. EEG, a noninvasive BCI method, uses many electrodes on the scalp, and a large number of electrodes make the resulting analysis difficult. In addition, traditional EEG analysis cannot handle multiple stimuli. On the other hand, the classification method using the EEG signal has a low accuracy. To solve these problems, we use a heuristic approach to reduce the complexities in multichannel problems and classification. In this study, we build a group of stimuli using the HS algorithm. Then, the features from common spatial patterns are classified by the HS classifier. To confirm the proposed method, we perform experiments using 64-channel EEG equipment. The subjects are subjected to three kinds of stimuli: audio, visual, and motion. Each stimulus is applied alone or in combination with the others. The acquired signals are processed by the proposed method. The classification results in an accuracy of approximately 63%. We conclude that the heuristic approach using the HS algorithm on the BCI is beneficial for EEG signal analysis.

Online Adaptation of a c-VEP Brain-Computer Interface(BCI) Based on Error-Related Potentials and Unsupervised Learning

The goal of a Brain-Computer Interface (BCI) is to control a computer by pure brain activity. Recently, BCIs based on code-modulated visual evoked potentials (c-VEPs) have shown great potential to establish high-performance communication. In this paper we present a c-VEP BCI that uses online adaptation of the classifier to reduce calibration time and increase performance. We compare two different approaches for online adaptation of the system: an unsupervised method and a method that uses the detection of error-related potentials. Both approaches were tested in an online study, in which an average accuracy of 96% was achieved with adaptation based on error-related potentials. This accuracy corresponds to an average information transfer rate of 144 bit/min, which is the highest bitrate reported so far for a non-invasive BCI. In a free-spelling mode, the subjects were able to write with an average of 21.3 error-free letters per minute, which shows the feasibility of the BCI system in a normal-use scenario. In addition we show that a calibration of the BCI system solely based on the detection of error-related potentials is possible, without knowing the true class labels.

Electrocorticographic (ECoG) correlates of human arm movements

Invasive and non-invasive brain-computer interface (BCI) studies have long focused on the motor cortex for kinematic control of artificial devices. Most of these studies have used single-neuron recordings or electroencephalography (EEG). Electrocorticography (ECoG) is a relatively new recording modality in BCI research that has primarily been built on successes in EEG recordings. We built on prior experiments related to single-neuron recording and quantitatively compare the extent to which different brain regions reflect kinematic tuning parameters of hand speed, direction, and velocity in both a reaching and tracing task in humans. Hand and arm movement experiments using ECoG have shown positive results before, but the tasks were not designed to tease out which kinematics are encoded. In non-human primates, the relationships among these kinematics have been more carefully documented, and we sought to begin elucidating that relationship in humans using ECoG. The largest modulation in ECoG activity for direction, speed, and velocity representation was found in the primary motor cortex. We also found consistent cosine tuning across both tasks, to hand direction and velocity in the high gamma band (70-160Hz). Thus, the results of this study clarify the neural substrates involved in encoding aspects of motor preparation and execution and confirm the important role of the motor cortex in BCI applications.

Shared electrophysiology mechanisms of body ownership and motor imagery

Although we feel, see, and experience our hands as our own (body or hand ownership), recent research has shown that illusory hand ownership can be induced for fake or virtual hands and may be useful for neuroprosthetics and brain-computer interfaces. Despite the vast amount of behavioral data on illusory hand ownership, neuroimaging studies are rare, in particular electrophysiological studies. Thus, while the neural systems underlying hand ownership are relatively well described, the spectral signatures of body ownership as measured by electroencephalography (EEG) remain elusive. Here we induced illusory hand ownership in an automated, computer-controlled manner using virtual reality while recording 64-channel EEG and found that illusory hand ownership is reflected by a body-specific modulation in the mu-band over fronto-parietal cortex. In a second experiment in the same subjects, we then show that mu as well as beta-band activity in highly similar fronto-parietal regions was also modulated during a motor imagery task often used in paradigms employing non-invasive brain-computer interface technology. These data provide insights into the electrophysiological brain mechanisms of illusory hand ownership and their strongly overlapping mechanisms with motor imagery in fronto-parietal cortex. They also highlight the potential of combining high-resolution EEG with virtual reality setups and automatized stimulation protocols for systematic, reproducible stimulus presentation in cognitive neuroscience, and may inform the design of non-invasive brain-computer interfaces.