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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.

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.

We have examined a role of dynamic synapses in the stochastic Hop eldlike 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 phenomeno n might reect the exibility of real neural systems and their readiness to receive and respond to novel and changing external stimuli.

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 facilitationthat 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.161

We developed an objective and automatic procedure to assess the severity of levodopa-induced dyskinesia (LID) in patients with Parkinson's 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.

Abstract:We developed an algorithm that distinguishes between on and off states in patients with Parkinson's 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 Parkinson's disease patients and can operate successfully in unsupervised ambulatory conditions. © 2005 Movement Disorder Society Key words: Parkinson's disease; motor fluctuations; automatic assessment; accelerometers; daily life During the first years of levodopa (L-dopa) treatment, patients with Parkinson's disease (PD) have a stable response to L-dopa. After several years of L-dopa treatment, however, an increasing number of patients show fluctuations in motor response and L-dopa-induced dyskinesias (LID). 1-4 These complications constitute a major problem in the long-term management of PD and add substantially to the patient's disability. Two main problems arise from a therapeutical point of view: first, the clinical state of patients has to be determined (on, off, or LID), and second, it has to be known how this clinical state fluctuates over time during the course of the day. Many methods have been developed to assess these late L-dopa problems in PD; however, the standard clinical detection and rating methods can only be applied in a hospital setting under supervision of a trained clinical observer. [5][6][7] Moreover, these rating methods provide only a momentary assessment of the clinical condition. This is not sufficient for practical purposes, because fluctuations over time require long-term supervision of up to a few days.For long-term evaluation of PD symptoms, patients usually have to keep a diary to record whether they are on, have LID, or are off (reemergence of PD symptoms). However, self-report of the motor-state in diaries has several limitations and can be troublesome or even unreliable. 8 -10 For example, Goetz and colleagues 11 tested the efficacy of a patient-training videota...

A model of a topologically organized neural network of a Hop.field 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.