Brain-computer communication: motivation, aim, and impact of exploring a virtual apartment.

The step away from a synchronized or cue-based brain-computer interface (BCI) and from laboratory conditions towards real world applications is very important and crucial in BCI research. This work shows that ten naive subjects can be trained in a synchronous paradigm within three sessions to navigate freely through a virtual apartment, whereby at every junction the subjects could decide by their own, how they wanted to explore the virtual environment (VE). This virtual apartment was designed similar to a real world application, with a goal-oriented task, a high mental workload, and a variable decision period for the subject. All subjects were able to perform long and stable motor imagery over a minimum time of 2 s. Using only three electroencephalogram (EEG) channels to analyze these imaginations, we were able to convert them into navigation commands. Additionally, it could be demonstrated that motivation is a very crucial factor in BCI research; motivated subjects perform much better than unmotivated ones.     

Continuous EEG classification during motor imagery-simulation of an asynchronous BCI

Nearly all electroencephalogram (EEG)-based brain-computer interface (BCI) systems operate in a cue-paced or synchronous mode. This means that the onset of mental activity (thought) is externally-paced and the EEG has to be analyzed in predefined time windows. In the near future, BCI systems that allow the user to intend a specific mental pattern whenever she/he wishes to produce such patterns will also become important. An asynchronous BCI is characterized by continuous analyzing and classification of EEG data. Therefore, it is important to maximize the hits (true positive rate) during an intended mental task and to minimize the false positive detections in the resting or idling state. EEG data recorded during right/left motor imagery is used to simulate an asynchronous BCI. To optimize the classification results, a refractory period and a dwell time are introduced.     

Information transfer rate in a five-classes brain-computerinterface

The information transfer rate, given in bits per trial, is used as an evaluation measurement in a brain-computer interface (BCI). Three subjects performed four motor-imagery (left hand, right hand, foot, and tongue) and one mental-calculation task. Classification of the EEG patterns is based on band power estimates and hidden Markov models. We propose a method that combines the EEG patterns based on separability into subsets of two, three, four, and five mental tasks. The information transfer rates of the BCI systems comprised of these subsets are reported. The achieved information transfer rates vary from 0.42 to 0.81 bits per trial and reveal that the upper limit of different mental tasks for a BCI system is three. In each subject, different combinations of three tasks resulted in the best performance     

Optimal spatial filtering of single trial EEG during imagined hand movement.

The development of an electroencephalograph (EEG)-based brain-computer interface (BCI) requires rapid and reliable discrimination of EEG patterns, e.g., associated with imaginary movement. One-sided hand movement imagination results in EEG changes located at contra- and ipsilateral central areas. We demonstrate that spatial filters for multichannel EEG effectively extract discriminatory information from two populations of single-trial EEG, recorded during left- and right-hand movement imagery. The best classification results for three subjects are 90.8%, 92.7%, and 99.7%. The spatial filters are estimated from a set of data by the method of common spatial patterns and reflect the specific activation of cortical areas. The method performs a weighting of the electrodes according to their importance for the classification task. The high recognition rates and computational simplicity make it a promising method for an EEG-based brain-computer interface.     

Foot and hand area mu rhythms

Spontaneous EEG can display spatio-temporal patterns of desynchronized or synchronized alpha band activity. Event-related desynchronization (ERD) of rhythms within alpha and lower beta bands is characteristic of activated cortical areas ready to process information or to prepare a movement, while event-related synchronization (ERS) in the same frequency bands can be seen as an electrophysiological correlate of resting or idling cortical areas. EEG was investigated over primary sensorimotor and premotor areas during discrete hand and foot movements. ERD was found over the primary hand area during finger movement and over the primary foot area during toe movement. The former was observed in every subject, the latter was more difficult to find. From these results it can be speculated that each primary sensorimotor area has its own intrinsic rhythm, which becomes desynchronized when the corresponding area is activated. ERS, in the form of an enhanced mu rhythm on electrodes overlying the primary hand area, was observed not only during visual processing but also during foot movement. In both cases, the hand area is not needed to perform a task and, therefore, can be considered to be in an idling state. The supplementary motor area (SMA) also plays an important role in preparation and planning of movement. It is demonstrated that this area also displays rhythmic activity within the alpha band, that is both linearly and non-linearly phase coupled to the intrinsic (mu) rhythm of the primary hand area. With planning and preparation of movement, this SMA rhythm is desynchronized and also the degree of coupling between the two areas decreases.     

Detection of movement-related patterns in ongoing single-channel electrocorticogram

Adaptive autoregressive parameters and a linear classifier were used to detect movement related desynchronization and synchronization patterns in single-channel electrocorticogram (ECoG) obtained from implanted electrode grids. The best classification accuracies found had more than 90% hits and less than 10% false positives. The findings show that the detection of event-related desynchronization and synchronization in ECoG data can be used to reliably provide switch control directly by the brain and is therefore very suitable as the basis of a direct brain interface.     

Study of on-line adaptive discriminant analysis for EEG-based brain computer interfaces

A study of different on-line adaptive classifiers, using various feature types is presented. Motor imagery brain computer interface (BCI) experiments were carried out with 18 naive able-bodied subjects. Experiments were done with three two-class, cue-based, electroencephalogram (EEG)-based systems. Two continuously adaptive classifiers were tested: adaptive quadratic and linear discriminant analysis. Three feature types were analyzed, adaptive autoregressive parameters, logarithmic band power estimates and the concatenation of both. Results show that all systems are stable and that the concatenation of features with continuously adaptive linear discriminant analysis classifier is the best choice of all. Also, a comparison of the latter with a discontinuously updated linear discriminant analysis, carried out in on-line experiments with six subjects, showed that on-line adaptation performed significantly better than a discontinuous update. Finally a static subject-specific baseline was also provided and used to compare performance measurements of both types of adaptation.     

Using time-dependent neural networks for EEG classification.

This paper compares two different topologies of neural networks. They are used to classify single trial electroencephalograph (EEG) data from a brain-computer interface (BCI). A short introduction to time series classification is given, and the used classifiers are described. Standard multilayer perceptrons (MLPs) are used as a standard method for classification. They are compared to finite impulse response (FIR) MLPs, which use FIR filters instead of static weights to allow temporal processing inside the classifier. A theoretical comparison of the two architectures is presented. The results of a BCI experiment with three different subjects are given and discussed. These results demonstrate the higher performance of the FIR MLP compared with the standard MLP.     

EEG-based communication: evaluation of alternative signal prediction methods

Individuals can learn to control the amplitude of EEG activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. For one-dimensional (i.e., vertical) cursor movement, a linear equation translates the EEG activity into cursor movement. To translate an individual's EEG control into cursor control as effectively as possible, the intercept in this equation, which determines whether upward or downward movement occurs, should be set so that top and bottom targets are equally accessible. The present study compares alternative methods for using an individual's previous performance to select the intercept for subsequent trials. In offline analyses, five different intercept selection methods were applied to EEG data collected while trained subjects were moving the cursor to targets at the top or bottom edge of the screen. In the first two methods-moving average, and weighted sum-a single intercept was selected for the entire 1-2 sec period of each trial. In the other three methods-blocked moving average, blocked weighted sum, and blocked recursive sum (a variation of the weighted sum)-an intercept was selected for each 200-ms segment of the trial. The results from these methods were compared in regard to their balance between upward and downward movements and their consistency of performance across trials. For all subjects combined, the five methods performed similarly. However, performance across subjects was more consistent for the moving average, blocked moving average, and blocked recursive sum methods than for the weighted sum and blocked weighted sum methods. Due to its consistent performance and its computational simplicity, the moving average method, using the five most recent pairs of top and bottom trials, appears to be the method of choice.     

Brain–Computer Communication: Motivation, Aim, and Impact of Exploring a Virtual Apartment

The step away from a synchronized or cue-based brain-computer interface (BCI) and from laboratory conditions towards real world applications is very important and crucial in BCI research. This work shows that ten naive subjects can be trained in a synchronous paradigm within three sessions to navigate freely through a virtual apartment, whereby at every junction the subjects could decide by their own, how they wanted to explore the virtual environment (VE). This virtual apartment was designed similar to a real world application, with a goal-oriented task, a high mental workload, and a variable decision period for the subject. All subjects were able to perform long and stable motor imagery over a minimum time of 2 s. Using only three electroencephalogram (EEG) channels to analyze these imaginations, we were able to convert them into navigation commands. Additionally, it could be demonstrated that motivation is a very crucial factor in BCI research; motivated subjects perform much better than unmotivated ones.