Oscillatory activity in the medial prefrontal cortex and nucleus accumbens correlates with impulsivity and reward outcome.

Actions expressed prematurely without regard for their consequences are considered impulsive. Such behaviour is governed by a network of brain regions including the prefrontal cortex (PFC) and nucleus accumbens (NAcb) and is prevalent in disorders including attention deficit hyperactivity disorder (ADHD) and drug addiction. However, little is known of the relationship between neural activity in these regions and specific forms of impulsive behaviour.

Review of machine learning and signal processing techniques for automated electrode selection in high-density microelectrode arrays.

Recently developed CMOS-based microprobes contain hundreds of electrodes on a single shaft with interelectrode distances as small as 30 µm. So far, neuroscientists manually select a subset of those electrodes depending on their appraisal of the "usefulness" of the recorded signals, which makes the process subjective but more importantly too time consuming to be useable in practice. The ever-increasing number of recording electrodes on microelectrode probes calls for an automated selection of electrodes containing "good quality signals" or "signals of interest.

Probabilistic identification of cerebellar cortical neurones across species.

Despite our fine-grain anatomical knowledge of the cerebellar cortex, electrophysiological studies of circuit information processing over the last fifty years have been hampered by the difficulty of reliably assigning signals to identified cell types. We approached this problem by assessing the spontaneous activity signatures of identified cerebellar cortical neurones. A range of statistics describing firing frequency and irregularity were then used, individually and in combination, to build Gaussian Process Classifiers (GPC) leading to a probabilistic classification of each neurone type and the computation of equi-probable decision boundaries between cell classes.

Enhancing the yield of high-density electrode arrays through automated electrode selection.

Recently developed CMOS-based microprobes contain hundreds of electrodes on a single shaft with inter-electrode distances as small as 30 μm. So far, neuroscientists needed to select electrodes manually from hundreds of electrodes. Here we present an electronic depth control algorithm that allows to select electrodes automatically, hereby allowing to reduce the amount of data and locating those electrodes that are close to neurons.

Information theory filters for wavelet packet coefficient selection with application to corrosion type identification from acoustic emission signals.

The damage caused by corrosion in chemical process installations can lead to unexpected plant shutdowns and the leakage of potentially toxic chemicals into the environment. When subjected to corrosion, structural changes in the material occur, leading to energy releases as acoustic waves. This acoustic activity can in turn be used for corrosion monitoring, and even for predicting the type of corrosion.

Control and data acquisition software for high-density CMOS-based microprobe arrays implementing electronic depth control.

This paper presents the NeuroSelect software for managing the electronic depth control of cerebral CMOS-based microprobes for extracellular in vivo recordings. These microprobes contain up to 500 electronically switchable electrodes which can be appropriately selected with regard to specific neuron locations in the course of a recording experiment. NeuroSelect makes it possible to scan the electrodes electronically and to (re)select those electrodes of best signal quality resulting in a closed-loop design of a neural acquisition system.

Posterior probability profiles for the automated assessment of the recovery of patients with stroke from activity of daily living tasks.

Objective: Assessing recovery after stroke has been so far a time consuming procedure in which trained clinicians are required. A demand for automated assessment techniques arises due to the increasing number of patients with stroke and the continuous growth of new treatment options. In this study, we investigate the applicability of isometric force and torque measurements in activity of daily living tasks to assess the functional recovery after stroke in an automated way.

Hybrid feature subset selection for the quantitative assessment of skills of stroke patients in activity of daily living tasks.

Stroke patients have a decreased ability in performing activity of daily living (ADL) tasks such as in 'drinking a glass of water', 'turning a key', 'picking up a spoon', 'lifting a bag', 'reaching a bottle' and 'lifting and carrying a bottle'. These tasks can be quantified by measuring forces and torques exerted on the objects. However, the resulting force and torque time series represent information at a very low level of abstraction and don't inform clinicians what really distinguishes patients from normal controls in performing these tasks.

Statistically rigorous human movement onset detection with the maximal information redundancy criterion.

Stroke patients have a decreased ability in performing activity of daily living (ADL) tasks such as in "drinking a glass of water", "lifting a bag", "turning a key" and so on. Sensorimotor force and torque measurements from patients performing these standardized ADL tasks are hypothesized to give quantitative information about the recovery process. Parts of the force/torque measurements contain useful information, when related to the initiation of the movement during ADL tasks.