The Berlin brain-computer interface: EEG-based communication without subject training (original) (raw)
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The Berlin Brain-Computer Interface: Machine Learning Based Detection of User Specific Brain States
J. Univers. Comput. Sci., 2006
The Berlin Brain-Computer Interface (BBCI) project develops an EEG-based BCI system that uses machine learning techniques to adapt to the specific brain signatures of each user. This concept allows to achieve high quality feedback already in the very first session without subject training. Here we present the broad range of investigations and experiments that have been performed within the BBCI project. The first kind of experiments analyzes the predictability of performing limbs from the premovement (readiness) potentials including successful feedback experiments. The limits with respect to the spatial resolution of the somatotopy are explored by contrasting brain patterns of movements of (1) left vs. right foot, (2) index vs. little finger within one hand, and (3) finger vs. wrist vs. elbow vs. shoulder within one arm. A study of phantom movements of patients with traumatic amputations shows the potential applicability of this BCI approach. In a complementary approach, voluntary m...
Improving speed and accuracy of brain-computer interfaces using readiness potential features
The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
To enhance human interaction with machines, research interest is growing to develop a 'Brain-Computer Interface', which allows communication of a human with a machine only by use of brain signals. So far, the applicability of such an interface is strongly limited by low bit-transfer rates, slow response times and long training sessions for the subject. The Berlin Brain-Computer Interface (BBCI) project is guided by the idea to train a computer by advanced machine learning techniques both to improve classification performance and to reduce the need of subject training. In this paper we present two directions in which Brain-Computer Interfacing can be enhanced by exploiting the lateralized readiness potential: (1) for establishing a rapid response BCI system that can predict the laterality of upcoming finger movements before EMG onset even in time critical contexts, and (2) to improve information transfer rates in the common BCI approach relying on imagined limb movements.
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006
We summarize results from a series of related studies that aim to develop a motor-imagery-based brain-computer interface using a single recording session of EEG or ECoG signals for each subject. We apply the same experimental and analytical methods to 11 non-paralysed subjects (8 EEG, 3 ECoG), and to 5 paralysed subjects (4 EEG, 1 ECoG) who had been unable to communicate for some time. While it was relatively easy to obtain classifiable signals quickly from most of the non-paralysed subjects, it proved impossible to classify the signals obtained from the paralysed patients by the same methods. This highlights the fact that though certain BCI paradigms may work well with healthy subjects, this does not necessarily indicate success with the target user group. We outline possible reasons for this failure to transfer.
The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects
IEEE Transactions on Biomedical Engineering, 2008
The Berlin Brain-Computer Interface (BBCI) project develops a non-invasive BCI system whose key features are (1) the use of well-established motor competences as control paradigms, (2) high-dimensional features from multi-channel EEG and (3) advanced machine learning techniques. Spatiospectral changes of sensorimotor rhythms are used to discriminate imagined movements (left hand, right hand, foot). A previous feedback study ([1]) with 10 subjects provided preliminary evidence that the BBCI system can be operated at high accuracy for subjects with less than 5 prior BCI exposures. Here, we demonstrate in a group of 14 fully BCI-naïve subjects that 8/14 BCI novices can perform at >84% accuracy in their very first BCI session, and a further 4 subjects >70%. Thus, 12/14 BCI-novices had significant above-chance level performances without any subject training even in the first session, as based on an optimized EEG analysis by advanced machine learning algorithms.
Rapid prototyping of an EEG-based brain-computer interface (BCI)
Neural Systems and …, 2001
AbstractThe electroencephalogram (EEG) is modified by motor imagery and can be used by patients with severe motor impairments (eg, late stage of amyotrophic lateral sclerosis) to communicate with their environment. Such a direct connection between the brain and the computer is ...
Over 2 million people are affected by neural diseases such as multiple Sclerosis, Amyotrophic Lateral Sclerosis, spinal cord injury, cerebral palsy, and other diseases impairing the neural pathways that control muscles. Indeed, these diseases cause severe paralysis and the persons suffer from what is called "Locked in syndrom". Consequently, a Brain Computer Interface noted BCI can be used as an alternative communication channel. This project belongs to a Brain Computer Interface research. More precisely, it focuses on the development of noninvasive platform of electroencephalographic (EEG) signals in terms of acquisition, pre-processing, feature extraction for providing an alternative communication or control channel for patient with severe motor disabilities.
The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology
Frontiers in Neuroscience, 2010
Brain-computer interfacing (BCI) is a steadily growing area of research. While initially BCI research was focused on applications for paralyzed patients, increasingly more alternative applications in healthy human subjects are proposed and investigated. In particular, monitoring of mental states and decoding of covert user states have seen a strong rise of interest. Here, we present some examples of such novel applications which provide evidence for the promising potential of BCI technology for non-medical uses. Furthermore, we discuss distinct methodological improvements required to bring non-medical applications of BCI technology to a diversity of layperson target groups, e.g., ease of use, minimal training, general usability, short control latencies.
Towards Improved EEG Interpretation for the Control of a Prosthetic/Orthotic Hand using a BCI
A brain-computer interface (BCI) can be used to control a prosthetic or orthotic hand using neural activity from the brain in order to allow people who suffer from motor impairments to perform simple daily tasks and thus improve their quality of life. The core of this sensorimotor BCI lies in the interpretation of the neural information extracted from electroencephalogram (EEG). It is desired to improve on the interpretation of the EEG signals for five essential hand movements i.e. wrist extension and flexion, finger extension and flexion and the tripod pinch. This paper focuses on the intermediate step of differentiating between the right and left combinations of these hand movements. EEG data was recorded from test subjects as they executed and imagined the five essential hand movements using both hands. Independent component analysis (ICA) and time-frequency techniques were used to extract spectral features based on event-related (de)synchronisation (ERD/ERS) and movement-related cortical potentials (MRCP). These are referred to as TFSE and TFSM features respectively. The Bhattacharyya distance (BD) was used for feature reduction, while Mahalanobis distance (MD) clustering and artificial neural networks (ANN) were used as basic classifiers. A group classifier was used to combine the TFSE and TFSM features (COMB). Best average accuracies of 88 %, 77 % and 88 % were obtained for TFSE, TFSM and COMB features respectively for imagined movements. The research shows that EEG discrimination between right and left hand movements is comparable to similar BCI research and that feature combination holds some promise.
Severe motor impairment and disability can be caused by many clinical situations and constitutes a challenge yet unmet by contemporary medicine 1 . Ranging from appendix movement loss to high spinal cord damage and from upstarting Amyotrophic Lateral Sclerosis (ALS) to complete locked-in syndrome, these situations mostly remain effectively uncured. Patients in those cases share one common characteristic: a severed link between thought and action, meaning a block between the transmission of the patient's will to move, speak or otherwise communicate with her surroundings (thought) and the actual movement, speech and communication (action). Bridging those two elements has been a focal point of many research fields, including pharmacology, biology and genetics and, lately, neuroinformatics. The concept of Man-Machine Interfaces (MMI) has been under research essentially since the 80s 2 (and perceived soon after by science fiction), but only in the 90s and onwards has solidified itself into the formed, separate scientific field of Human-Computer Interaction (HCI) and, more specifically and importantly, the field of Brain-Computer Interfaces (BCI) 3 . Those interfaces aim to somehow "restore" the loss of brain-environment communication and bypass the cause of that loss, thereby posing as a promising solution/treatment to the aforementioned medical conditions. Brain Computer Interfaces are systems that use brain activity to interpret voluntary movement thought to control of external devices such as, computer cursors and computers, wheelchairs and neuroprosthetics and robotic arms 4 . Brain activity is identified by electric, magnetic or metabolic brain signals as extracted by depictive methods already used in medicine and is classified, analysed and translated by computer software to be appended to device control functions. Among those methods, electroencephalography (EEG) ABStrAct: Restoration of motor functions of patients with loss of mobility constitutes a yet unsolved medical problem, but also one of the most prominent research areas of neurosciences. Among suggested solutions, Brain Computer Interfaces have received much attention. BCI systems use electric, magnetic or metabolic brain signals to allow for control of external devices, such as wheelchairs, computers or neuroprosthetics, by disabled patients. Clinical applications includespinal cord injury, cerebrovascular accident rehabilitation, Amyotrophic Lateral Sclerosis patients. Various BCI systems are under research, facilitated by numerous measurement techniques including EEG, fMRI, MEG, nIRS and ECoG, each with its own advantages and disadvantages. Current research effort focuses on brain signal identification and extraction. Virtual Reality environments are also deployed for patient training. Wheelchair or robotic arm control has showed up as the first step towards actual mobility restoration. The next era of BCI research is envisaged to lie along the transmission of brain signals to systems that will control and restore movement of disabled patients via mechanical appendixes or directly to the muscle system by neurosurgical means.