Christoph Reichert

Single trial discrimination of individual finger movements on one hand: a combined MEG and EEG study.

It is crucial to understand what brain signals can be decoded from single trials with different recording techniques for the development of Brain-Machine Interfaces. A specific challenge for non-invasive recording methods are activations confined to small spatial areas on the cortex such as the finger representation of one hand. Here we study the information content of single trial brain activity in non-invasive MEG and EEG recordings elicited by finger movements of one hand. We investigate the feasibility of decoding which of four fingers of one hand performed a slight button press. With MEG we demonstrate reliable discrimination of single button presses performed with the thumb, the index, the middle or the little finger (average over all subjects and fingers 57%, best subject 70%, empirical guessing level: 25.1%). EEG decoding performance was less robust (average over all subjects and fingers 43%, best subject 54%, empirical guessing level 25.1%). Spatiotemporal patterns of amplitude variations in the time series provided best information for discriminating finger movements. Non-phase-locked changes of mu and beta oscillations were less predictive. Movement related high gamma oscillations were observed in average induced oscillation amplitudes in the MEG but did not provide sufficient information about the fingers identity in single trials. Importantly, pre-movement neuronal activity provided information about the preparation of the movement of a specific finger. Our study demonstrates the potential of non-invasive MEG to provide informative features for individual finger control in a Brain-Machine Interface neuroprosthesis.

Hierarchy of prediction errors for auditory events in human temporal and frontal cortex

Significance To survive, organisms must constantly form predictions of the future based on past regularities. When predictions are violated, action may be needed. Different scales of environmental regularity need to encompass both subsecond repetitions and complex structures spanning longer timescales. How different parts of the brain monitor these temporal regularities and produce prediction error signals is unclear. Utilizing subdural electrocorticographic electrodes with an auditory paradigm involving local and global regularities, we show that frontal cortex is sensitive to the big picture, responding with high γ-band activity exclusively to globally unpredictable changes, whereas the temporal cortex equally responds to any change in the immediate history. These results reveal a hierarchy of predictive coding recorded directly from the human brain. Predictive coding theories posit that neural networks learn statistical regularities in the environment for comparison with actual outcomes, signaling a prediction error (PE) when sensory deviation occurs. PE studies in audition have capitalized on low-frequency event-related potentials (LF-ERPs), such as the mismatch negativity. However, local cortical activity is well-indexed by higher-frequency bands [high-γ band (Hγ): 80–150 Hz]. We compared patterns of human Hγ and LF-ERPs in deviance detection using electrocorticographic recordings from subdural electrodes over frontal and temporal cortices. Patients listened to trains of task-irrelevant tones in two conditions differing in the predictability of a deviation from repetitive background stimuli (fully predictable vs. unpredictable deviants). We found deviance-related responses in both frequency bands over lateral temporal and inferior frontal cortex, with an earlier latency for Hγ than for LF-ERPs. Critically, frontal Hγ activity but not LF-ERPs discriminated between fully predictable and unpredictable changes, with frontal cortex sensitive to unpredictable events. The results highlight the role of frontal cortex and Hγ activity in deviance detection and PE generation.

Online tracking of the contents of conscious perception using real-time fMRI

Perception is an active process that interprets and structures the stimulus input based on assumptions about its possible causes. We use real-time functional magnetic resonance imaging (rtfMRI) to investigate a particularly powerful demonstration of dynamic object integration in which the same physical stimulus intermittently elicits categorically different conscious object percepts. In this study, we simulated an outline object that is moving behind a narrow slit. With such displays, the physically identical stimulus can elicit categorically different percepts that either correspond closely to the physical stimulus (vertically moving line segments) or represent a hypothesis about the underlying cause of the physical stimulus (a horizontally moving object that is partly occluded). In the latter case, the brain must construct an object from the input sequence. Combining rtfMRI with machine learning techniques we show that it is possible to determine online the momentary state of a subjects conscious percept from time resolved BOLD-activity. In addition, we found that feedback about the currently decoded percept increased the decoding rates compared to prior fMRI recordings of the same stimulus without feedback presentation. The analysis of the trained classifier revealed a brain network that discriminates contents of conscious perception with antagonistic interactions between early sensory areas that represent physical stimulus properties and higher-tier brain areas. During integrated object percepts, brain activity decreases in early sensory areas and increases in higher-tier areas. We conclude that it is possible to use BOLD responses to reliably track the contents of conscious visual perception with a relatively high temporal resolution. We suggest that our approach can also be used to investigate the neural basis of auditory object formation and discuss the results in the context of predictive coding theory.

Deep Brain stimulation of the Nucleus Basalis of Meynert attenuates early EEG components associated with defective sensory gating in patients with Alzheimer disease – a two-case study

Alzheimer′s disease (AD) is associated with deterioration of memory and cognitive function and a degeneration of neurons of the nucleus basalis of Meynert (NBM). The NBM is the major input source of acetylcholine (ACh) to the cortex. The decreasing cholinergic innervation of the cortex due to degeneration of the NBM might be the cause of loss of memory function. NBM‐Deep brain stimulation (NBM‐DBS) is considered to serve as a potential therapeutic option for patients with AD by supporting residual cholinergic transmission to stabilize oscillatory activity in memory‐relevant circuits. However, whether DBS could improve sensory memory functions in patients with AD is not clear. Here, in a passive auditory oddball paradigm, patients with AD (N = 2) listened to repetitive background tones (standard tones) randomly interrupted by frequency deviants in two blocks with NBM‐DBS OFF and then NBM‐DBS ON, while age‐matched healthy controls (N = 6) repeated the experiment twice. The mismatch negativity in NBM‐DBS OFF significantly differed from controls in both blocks, but not under NBM‐DBS, which was likely due to a pronounced P50 increase overlapping with the N1 in NBM‐DBS OFF. This early complex of EEG components recovered under stimulation to a normal level as defined by responses in controls. In this temporal interval, we found in patients with NBM‐DBS ON (but not with NBM‐DBS OFF) and in controls a strong repetition suppression effect to standard tones – with more attenuated responses to frequently repeated standard tones. This highlights the role of NBM‐DBS for sensory gating of familiar auditory information into sensory memory.

A Comparative Study on the Detection of Covert Attention in Event-Related EEG and MEG Signals to Control a BCI

In brain-computer interface (BCI) applications the detection of neural processing as revealed by event-related potentials (ERPs) is a frequently used approach to regain communication for people unable to interact through any peripheral muscle control. However, the commonly used electroencephalography (EEG) provides signals of low signal-to-noise ratio, making the systems slow and inaccurate. As an alternative noninvasive recording technique, the magnetoencephalography (MEG) could provide more advantageous electrophysiological signals due to a higher number of sensors and the magnetic fields not being influenced by volume conduction. We investigated whether MEG provides higher accuracy in detecting event-related fields (ERFs) compared to detecting ERPs in simultaneously recorded EEG, both evoked by a covert attention task, and whether a combination of the modalities is advantageous. In our approach, a detection algorithm based on spatial filtering is used to identify ERP/ERF components in a data-driven manner. We found that MEG achieves higher decoding accuracy (DA) compared to EEG and that the combination of both further improves the performance significantly. However, MEG data showed poor performance in cross-subject classification, indicating that the algorithms ability for transfer learning across subjects is better in EEG. Here we show that BCI control by covert attention is feasible with EEG and MEG using a data-driven spatial filter approach with a clear advantage of the MEG regarding DA but with a better transfer learning in EEG.

Efficient recognition of event-related potentials in high-density MEG recordings

In brain-computer interfacing (BCI), the recognition of task-specific event-related potentials such as P300 responses is an established approach to regaining communication in severely paralyzed people. However, a reliable detection of single trial potentials is challenging, because they are strongly affected by noise. Furthermore, potentials with their subcomponents are often distributed over several channels. With high density sensor arrays, a hypothesis-driven selection of channels, as often performed in BCIs based on electroencephalography (EEG), is challenging. We present a new data-driven approach that constructs spatio-temporal filters, considerably reducing the number of channels, reducing noise, and simultaneously determining the underlying brain dynamics. The extracted signals can be easily used to recognize the event sequence on which users focus their attention, without applying multivariate classification. We evaluated the approach using high density magnetoencephalography (MEG) data, recorded during a BCI experiment based on P300 responses. Compared to the subjects performance achieved with the initial decoding approach, the recognition rate increased significantly from 74.1% (std: 14.8%) to 95.1% (std: 4.9%) correct detections, which implies an information transfer rate improvement from 6.9 bit/min to 13.1 bit/min on average over 17 subjects.

Brain-Controlled Selection of Objects Combined with Autonomous Robotic Grasping

A Brain–Computer Interface (BCI) could help to restore mobility of severely paralyzed patients, for instance by prosthesis control. However, the currently achievable information transfer rate of noninvasive BCIs is insufficient to control complex prostheses continuously in many degrees of freedom. In this paper we present an autonomous system for grasping natural objects that compensates the low information flow from noninvasive BCIs. Using this system, one out of several objects can be grasped without any muscle activity. Rather, the grasp is initiated by decoded voluntary brain wave modulations. Object selection and grasping are performed in a virtual reality environment. A universal grasp planning algorithm calculates the trajectory of a gripper online. The system can be controlled after less than 10 min of training. We found that decoding accuracy increases over time and that an increased sense of agency achieved by permitting free selections renders the system to work most reliably.

Direct evidence for prediction signals in frontal cortex independent of prediction error

Predictive coding (PC) has been suggested as one of the main mechanisms used by brains to interact with complex environments. PC theories posit top-down prediction signals, which are compared with actual outcomes, yielding in turn prediction-error signals, which are used, bottom-up, to modify the ensuing predictions. However, disentangling prediction from prediction-error signals has been challenging. Critically, while many studies found indirect evidence for predictive coding in the form of prediction-error signals, direct evidence for the prediction signal is mostly lacking. Here we provide clear evidence, obtained from intracranial cortical recordings in human surgical patients, that the human lateral prefrontal cortex generates prediction signals while anticipating an event. Patients listened to task-irrelevant sequences of repetitive tones including infrequent predictable or unpredictable pitch deviants. The amplitude of high frequency broadband (HFB) neural activity was decreased prior to the onset of expected relative to unexpected deviants in the frontal cortex only, and its amplitude was sensitive to the increasing likelihood of deviants following longer trains of standards in the unpredictable condition. Single trial HFB amplitudes predicted deviations and correlated with post-stimulus response to deviations. These results provide direct evidence for frontal cortex prediction signals independent of prediction-error signals.

An Efficient Decoder for the Recognition of Event-Related Potentials in High-Density MEG Recordings

Brain–computer interfacing (BCI) is a promising technique for regaining communication and control in severely paralyzed people. Many BCI implementations are based on the recognition of task-specific event-related potentials (ERP) such as P300 responses. However, because of the high signal-to-noise ratio in noninvasive brain recordings, reliable detection of single trial ERPs is challenging. Furthermore, the relevant signal is often heterogeneously distributed over several channels. In this paper, we introduce a new approach for recognizing a sequence of attended events from multi-channel brain recordings. The framework utilizes spatial filtering to reduce both noise and signal space considerably. We introduce different models that can be used to construct the spatial filter and evaluate the approach using magnetoencephalography (MEG) data involving P300 responses, recorded during a BCI experiment. Compared to the accuracy achieved in the BCI experiment performed without spatial filtering, the recognition rate increased significantly to up to 95.3% on average (SD: 5.3%). In combination with the data-driven spatial filter construction we introduce here, our framework represents a powerful method to reliably recognize a sequence of brain potentials from high-density electrophysiological data, which could greatly improve the control of BCIs.