Stan C. A. M. Gielen

The brain–computer interface cycle

Brain-computer interfaces (BCIs) have attracted much attention recently, triggered by new scientific progress in understanding brain function and by impressive applications. The aim of this review is to give an overview of the various steps in the BCI cycle, i.e., the loop from the measurement of brain activity, classification of data, feedback to the subject and the effect of feedback on brain activity. In this article we will review the critical steps of the BCI cycle, the present issues and state-of-the-art results. Moreover, we will develop a vision on how recently obtained results may contribute to new insights in neurocognition and, in particular, in the neural representation of perceived stimuli, intended actions and emotions. Now is the right time to explore what can be gained by embracing real-time, online BCI and by adding it to the set of experimental tools already available to the cognitive neuroscientist. We close by pointing out some unresolved issues and present our view on how BCI could become an important new tool for probing human cognition.

Neural network dynamics for path planning and obstacle avoidance

Abstract A model of a topologically organized neural network of a Hopfield type with nonlinear analog neurons is shown to be very effective for path planning and obstacle avoidance. This deterministic system can rapidly provide a proper path, from any arbitrary start position to any target position, avoiding both static and moving obstacles of arbitrary shape. The model assumes that an (external) input activates a target neuron, corresponding to the target position, and specifies obstacles in the topologically ordered neural map. The path follows from the neural network dynamics and the neural activity gradient in the topologically ordered map. The analytical results are supported by computer simulations to illustrate the performance of the network.

On the nature of intersensory facilitation of reaction time.

Two experiments examined the RT to visual stimuli presented alone and when either auditory (Experiment 1) or kinesthetic (Experiment 2) stimuli followed the visual event by 50 or 65 msec, respectively. As has been found before, the RT to combined stimulus events was 20 to 40 msec shorter than to visual events alone. While such results have generally been interpreted to mean that two sensory modalities are interacting, Raab’s (1962) hypothesis of statistical facilitation— that the subject responds to that stimulus modality whose processing is completed first—is also possible. Using Raab’s model, but with relaxed assumptions, the present experiments show that RT to combined stimulus events is more rapid than can be accounted for by statistical facilitation. Therefore, some intersensory interaction was probably occurring. The nature of these possible interactions and the status of the statistical-facilitation hypothesis are discussed. Supported in part by Grant BNS 80–23125 from the National Science Foundation to the second author.

Greater efficiency in attentional processing related to mindfulness meditation.

In this study, attentional processing in relation to mindfulness meditation was investigated. Since recent studies have suggested that mindfulness meditation may induce improvements in attentional processing, we have tested 20 expert mindfulness meditators in the attention network test. Their performance was compared to that of 20 age- and gender-matched controls. In addition to attentional network analyses, overall attentional processing was analysed by means of efficiency scores (i.e., accuracy controlled for reaction time). Better orienting and executive attention (reflected by smaller differences in either reaction time or error score, respectively) were observed in the mindfulness meditation group. Furthermore, extensive mindfulness meditation appeared to be related to a reduction of the fraction of errors for responses with the same reaction time. These results provide new insights into differences in attentional processing related to mindfulness meditation and suggest the possibility of increasing the efficiency in attentional processing by extensive mental training.

Kinematic properties of rapid aimed hand movements

Generalized motor program theory and the models of Schmidt, Zelaznik, and Frank (1978), and Meyer, Smith, and Wright (1982) of speed-accuracy relationships in aimed hand movements require that the underlying acceleration-time patterns exhibit time rescalability, in which all acceleration-time functions in an aimed hand movement are generated from one rescalable pattern. We examined this property as a function of movement time in Experiment 1, and as a function of movement time and movement distance in Experiment 2. Both experiments failed to demonstrate strict time rescalability in acceleration-time patterns, with the time to peak positive acceleration being invariant across movement time. This suggests that time rescalability is not a necessary condition for the linear relation between speed and spatial variability. A second major finding was that the variability in distance traveled at the end of positive acceleration was independent of movement time, contrary to the symmetric-impulse-variability model of Meyer et al. (1982). The findings of both experiments suggest that the processes involved in decelerating the limb play an important, but yet to be understood, role in determining the linear speed-accuracy trade-off. Finally, these results suggest that generalized motor programs are not based on simple, time-rescalable acceleration patterns.

Ambulatory motor assessment in Parkinson's disease

We developed an algorithm that distinguishes between on and off states in patients with Parkinsons disease during daily life activities. Twenty‐three patients were monitored continuously in a home‐like situation for approximately 3 hours while they carried out normal daily‐life activities. Behavior and comments of patients during the experiment were used to determine the on and off periods by a trained observer. Behavior of the patients was measured using triaxial accelerometers, which were placed at six different positions on the body. Parameters related to hypokinesia (percentage movement), bradykinesia (mean velocity), and tremor (percentage peak frequencies above 4 Hz) were used to distinguish between on and off states. The on–off detection was evaluated using sensitivity and specificity. The performance for each patient was defined as the average of the sensitivity and specificity. The best performance to classify on and off states was obtained by analysis of movements in the frequency domain with a sensitivity of 0.97 and a specificity of 0.97. We conclude that our algorithm can distinguish between on and off states with a sensitivity and specificity near 0.97. This method, together with our previously published method to detect levodopa‐induced dyskinesia, can automatically assess the motor state of Parkinsons disease patients and can operate successfully in unsupervised ambulatory conditions.

Automatic assessment of levodopa‐induced dyskinesias in daily life by neural networks

We developed an objective and automatic procedure to assess the severity of levodopa‐induced dyskinesia (LID) in patients with Parkinsons disease during daily life activities. Thirteen patients were continuously monitored in a home‐like situation for a period of approximately 2.5 hours. During this time period, the patients performed approximately 35 functional daily life activities. Behavior of the patients was measured using triaxial accelerometers, which were placed at six different positions on the body. A neural network was trained to assess the severity of LID using various variables of the accelerometer signals. Neural network scores were compared with the assessment by physicians, who evaluated the continuously videotaped behavior of the patients off‐line. The neural network correctly classified dyskinesia or the absence of dyskinesia in 15‐minute intervals in 93.7, 99.7, and 97.0% for the arm, trunk, and leg, respectively. In the few cases of misclassification, the rating by the neural network was in the class next to that indicated by the physicians using the AIMS score (scale 0–4). Analysis of the neural networks revealed several new variables, which are relevant for assessing the severity of LID. The results indicate that the neural network can accurately assess the severity of LID and could distinguish LID from voluntary movements in daily life situations.

Associative memory with dynamic synapses

We have examined a role of dynamic synapses in the stochastic Hopfield-like network behavior. Our results demonstrate an appearance of a novel phase characterized by quick transitions from one memory state to another. The network is able to retrieve memorized patterns corresponding to classical ferromagnetic states but switches between memorized patterns with an intermittent type of behavior. This phenomenon might reflect the flexibility of real neural systems and their readiness to receive and respond to novel and changing external stimuli.

Assessing Neuronal Coherence with Single-Unit, Multi-Unit, and Local Field Potentials

The purpose of this study was to obtain a better understanding of neuronal responses to correlated input, in particular focusing on the aspect of synchronization of neuronal activity. The first aim was to obtain an analytical expression for the coherence between the output spike train and correlated input and for the coherence between output spike trains of neurons with correlated input. For Poisson neurons, we could derive that the peak of the coherence between the correlated input and multi-unit activity increases proportionally with the square root of the number of neurons in the multi-unit recording. The coherence between two typical multi-unit recordings (2 to 10 single units) with partially correlated input increases proportionally with the number of units in the multi-unit recordings. The second aim of this study was to investigate to what extent the amplitude and signal-to-noise ratio of the coherence between input and output varied for single-unit versus multi-unit activity and how they are affected by the duration of the recording. The same problem was addressed for the coherence between two single-unit spike series and between two multi-unit spike series. The analytical results for the Poisson neuron and numerical simulations for the conductance-based leaky integrate-and-fire neuron and for the conductance-based Hodgkin-Huxley neuron show that the expectation value of the coherence function does not increase for a longer duration of the recording. The only effect of a longer duration of the spike recording is a reduction of the noise in the coherence function. The results of analytical derivations and computer simulations for model neurons show that the coherence for multi-unit activity is larger than that for single-unit activity. This is in agreement with the results of experimental data obtained from monkey visual cortex (V4). Finally, we show that multitaper techniques greatly contribute to a more accurate estimate of the coherence by reducing the bias and variance in the coherence estimate.

Task-dependent learning of attention

In this article, we propose a neural network model for selective covert visual attention. This model can learn to focus its attention on important features depending on the task to be fulfilled by gating the flow of information from the lower to the higher levels of the visual system. The model is kept as simple as possible, but it is still capable of reproducing attentional behavior observed in psychological experiments. Computer simulations demonstrate that (1) it can learn categories to reduce reaction time without a decrease in performance, (2) the model reveals a performance similar to that of humans in feature and conjunction search, and (3) its learning dynamics are comparable with those of humans. Copyright 1997 Elsevier Science Ltd.