Jörn Rickert

Inference of hand movements from local field potentials in monkey motor cortex.

The spiking of neuronal populations in motor cortex provides accurate information about movement parameters. Here we show that hand movement target and velocity can be inferred from multiple local field potentials (LFPs) in single trials approximately as efficiently as from multiple single-unit activity (SUA) recorded from the same electrodes. Our results indicate that LFPs can be used as an additional signal for decoding brain activity, particularly for new neuroprosthetic applications.

Encoding of Movement Direction in Different Frequency Ranges of Motor Cortical Local Field Potentials

Recent studies showed that the low-frequency component of local field potentials (LFPs) in monkey motor cortex carries information about parameters of voluntary arm movements. Here, we studied how different signal components of the LFP in the time and frequency domains are modulated during center-out arm movements. Analysis of LFPs in the time domain showed that the amplitude of a slow complex waveform beginning shortly before the onset of arm movement is modulated with the direction of the movement. Examining LFPs in the frequency domain, we found that direction-dependent modulations occur in three frequency ranges, which typically increased their amplitudes before and during movement execution: ≤4, 6–13, and 63–200 Hz. Cosine-like tuning was prominent in all signal components analyzed. In contrast, activity in a frequency band ≈30 Hz was not modulated with the direction of movement and typically decreased its amplitude during the task. This suggests that high-frequency oscillations have to be divided into at least two functionally different regimes: one ≈30 Hz and one >60 Hz. Furthermore, using multiple LFPs, we could show that LFP amplitude spectra can be used to decode movement direction, with the best performance achieved by the combination of different frequency ranges. These results suggest that using the different frequency components in the LFP is useful in improving inference of movement parameters from local field potentials.

Adaptive Classification for Brain Computer Interfaces

In this paper we evaluate the performance of a new adaptive classifier for the use within a brain computer-interface (BCI). The classifier can either be adaptive in a completely unsupervised manner or using unsupervised adaptation in conjunction with a neuronal evaluation signal to improve adaptation. The first variant, termed adaptive linear discriminant analysis (ALDA), updates mean values as well as covariances of the class distributions continuously in time. In simulated as well as experimental data ALDA substantially outperforms the non-adaptive LDA. The second variant, termed adaptive linear discriminant analysis with error correction (ALDEC), extends the unsupervised algorithm with an additional independent neuronal evaluation signal. Such a signal could be an error related potential which indicates when the decoder did not classify correctly. When the mean values of the class distributions circle around each other or even cross their way, ALDEC can yield a substantially better adaptation than ALDA depending on the reliability of the error signal. Given the non-stationarity of EEG signals during BCI control our approach might strongly improve the precision and the time needed to gain accurate control in future BCI applications.

Dynamic Encoding of Movement Direction in Motor Cortical Neurons

When we perform a skilled movement such as reaching for an object, we can make use of prior information, for example about the location of the object in space. This helps us to prepare the movement, and we gain improved accuracy and speed during movement execution. Here, we investigate how prior information affects the motor cortical representation of movements during preparation and execution. We trained two monkeys in a delayed reaching task and provided a varying degree of prior information about the final target location. We decoded movement direction from multiple single-unit activity recorded from M1 (primary motor cortex) in one monkey and from PMd (dorsal premotor cortex) in a second monkey. Our results demonstrate that motor cortical cells in both areas exhibit individual encoding characteristics that change dynamically in time and dependent on prior information. On the population level, the information about movement direction is at any point in time accurately represented in a neuronal ensemble of time-varying composition. We conclude that movement representation in the motor cortex is not a static one, but one in which neurons dynamically allocate their computational resources to meet the demands defined by the movement task and the context of the movement. Consequently, we find that the decoding accuracy decreases if the precise task time, or the previous information that was available to the monkey, were disregarded in the decoding process. An optimal strategy for the readout of movement parameters from motor cortex should therefore take into account time and contextual parameters.

An online brain–machine interface using decoding of movement direction from the human electrocorticogram

A brain-machine interface (BMI) can be used to control movements of an artificial effector, e.g. movements of an arm prosthesis, by motor cortical signals that control the equivalent movements of the corresponding body part, e.g. arm movements. This approach has been successfully applied in monkeys and humans by accurately extracting parameters of movements from the spiking activity of multiple single neurons. We show that the same approach can be realized using brain activity measured directly from the surface of the human cortex using electrocorticography (ECoG). Five subjects, implanted with ECoG implants for the purpose of epilepsy assessment, took part in our study. Subjects used directionally dependent ECoG signals, recorded during active movements of a single arm, to control a computer cursor in one out of two directions. Significant BMI control was achieved in four out of five subjects with correct directional decoding in 69%-86% of the trials (75% on average). Our results demonstrate the feasibility of an online BMI using decoding of movement direction from human ECoG signals. Thus, to achieve such BMIs, ECoG signals might be used in conjunction with or as an alternative to intracortical neural signals.

Invasive brain-machine interfaces: a survey of paralyzed patients' attitudes, knowledge and methods of information retrieval.

OBJECTIVE Brain-machine interfaces (BMI) are an emerging therapeutic option that can allow paralyzed patients to gain control over assistive technology devices (ATDs). BMI approaches can be broadly classified into invasive (based on intracranially implanted electrodes) and noninvasive (based on skin electrodes or extracorporeal sensors). Invasive BMIs have a favorable signal-to-noise ratio, and thus allow for the extraction of more information than noninvasive BMIs, but they are also associated with the risks related to neurosurgical device implantation. Current noninvasive BMI approaches are typically concerned, among other issues, with long setup times and/or intensive training. Recent studies have investigated the attitudes of paralyzed patients eligible for BMIs, particularly patients affected by amyotrophic lateral sclerosis (ALS). These studies indicate that paralyzed patients are indeed interested in BMIs. Little is known, however, about the degree of knowledge among paralyzed patients concerning BMI approaches or about how patients retrieve information on ATDs. Furthermore, it is not yet clear if paralyzed patients would accept intracranial implantation of BMI electrodes with the premise of decoding improvements, and what the attitudes of a broader range of patients with diseases such as stroke or spinal cord injury are towards this new kind of treatment. APPROACH Using a questionnaire, we surveyed 131 paralyzed patients for their opinions on invasive BMIs and their attitude toward invasive BMI treatment options. MAIN RESULTS The majority of the patients knew about and had a positive attitude toward invasive BMI approaches. The group of ALS patients was especially open to the concept of BMIs. The acceptance of invasive BMI technology depended on the improvements expected from the technology. Furthermore, the survey revealed that for paralyzed patients, the Internet is an important source of information on ATDs. SIGNIFICANCE Websites tailored to prospective BMI users should be further developed to provide reliable information to patients, and also to help to link prospective BMI users with researchers involved in the development of BMI technology.

Hints for a topographic map of tuning properties in primate motor cortex

The spatial organization of tuning properties of neurons in the primate motor cortex is still unknown. Here, we analyze the directional tuning of neurons and local field potentials recorded in the motor cortex of monkeys performing center out arm movements. We found that the tuning of nearby neurons, and of single neurons and local field potentials recorded from the same electrodes is more similar than expected by chance. These findings are in agreement with a spatial organization of the tuning properties in motor cortex.

Closed-loop interaction with the cerebral cortex using a novel micro-ECoG-based implant: the impact of beta vs. gamma stimulation frequencies on cortico-cortical spectral responses*

AbstractMedical brain implants for closed-loop interaction with the cerebral cortex promise new treatment options for brain disorders, and thus great efforts are being made to develop devices for long-term application. Closed-loop interaction can be implemented using electrophysiological recording techniques, and can be used to modulate local cortical activity or long-range functional connectivity. In a case study performed in sheep chronically implanted with a novel micro-electrocorticography-based device, we show that (1) open-loop single-pulse electrical stimulation (SPES) elicited the well-known cortico-cortical evoked potentials (CCEPs), and (2) closed-loop repetitive-pulse electrical stimulation (RPES) elicited specific cortico-cortical spectral responses (CCSRs). CCSRs were spatially focalized in the gamma band, compared with beta band independent of RPES frequency. The topography of CCSRs was different compared with CCEPs, suggesting that CCEPs and CCSRs capture different aspects of cortico-cortic...

Towards Electrocorticographic Electrodes for Chronic Use in BCI Applications

Electrocorticograms (ECoG) have been originally used for presurgical epilepsy monitoring. In the last years, it has been proven that the signals recorded from these electrodes also deliver signals that can be used within brain computer interface (BCI) applications. The state of the art of epicortical electrode arrays for neuroscientific research as well as for clinical applications is reviewed with respect to manufacturing techniques, spatial resolution and the ability to cover large areas of the brain surface. Results from epicortical studies show the feasibility to use ECoG BCI for several applications. We propose a new type of ECoG array that allows for high channel recording keeping the devices flexible and compliant. First results delivered promising results. For chronic BCI applications, wireless, fully implantable systems are mandatory. We summarize the target specifications and conclude with a personal opinion how these implants could look like in the near future.