ECoG Beta Suppression and Modulation During Finger Extension and Flexion

Neural oscillations originate predominantly from interacting cortical neurons and consequently reflect aspects of cortical information processing. However, their functional role is not yet fully understood and their interpretation is debatable. Amplitude modulations (AMs) in alpha (8–12 Hz), beta (13–30 Hz), and high gamma (70–150 Hz) band in invasive electrocorticogram (ECoG) and non-invasive electroencephalogram (EEG) signals change with behavior. Alpha and beta band AMs are typically suppressed (desynchronized) during motor behavior, while high gamma AMs highly correlate with the behavior. These two phenomena are successfully used for functional brain mapping and brain-computer interface (BCI) applications. Recent research found movement-phase related AMs (MPA) also in high beta/low gamma (24–40 Hz) EEG rhythms. These MPAs were found by separating the suppressed AMs into sustained and dynamic components. Sustained AM components are those with frequencies that are lower than the motor behavior. Dynamic components those with frequencies higher than the behavior. In this paper, we study ECoG beta/low gamma band (12–30 Hz/30–42 Hz) AM during repetitive finger movements addressing the question whether or not MPAs can be found in ECoG beta band. Indeed, MPA in the 12–18 Hz and 18–24 Hz band were found. This additional information may lead to further improvements in ECoG-based prediction and reconstruction of motor behavior by combining high gamma AM and beta band MPA

Band-specific features improve Finger Flexion Prediction from ECoG

International audienceAbstract—ECoG-based BCIs attract intensive attention recently. ECoG can provide a higher spatial resolution and signal quality compare to EEG recordings. These characteristics make possible to localize the source of neural signals precisely with respect to certain brain activities such that ECoG-based BCIs may realize a complex and apt neuroprosthesis. Signal processing is a very important task in the BCIs research for translating the brain signals into commands for a computer application or a neuroprosthesis. Here, we present a linear regression method based on the amplitude modulation of band-specific ECoG including tap delay for individual finger flexion prediction. We especially study the influence of the frequency band decomposition on the prediction. An efficient feature selection can reduce the number of features by a factor greater than 10 without a strong impact on the prediction. According to the experimental results, the gamm band (60-100Hz) seems the carry more useful information than the others. This method won the BCI competition IV dedicated to this mapping

Extraction of the Major Features of Brain Signals using Intelligent Networks

The brain-computer interface is considered one of the main tools for implementing and designing smart medical software. The analysis of brain signal data, called EEG, is one of the main tasks of smart medical diagnostic systems. While EEG signals have many components, one of the most important brain activities pursued is the P300 component. Detection of this component can help detect abnormalities and visualize the movement of organs of the body. In this research, a new method for processing EEG signals is proposed with the aim of detecting the P300 component. Major features were extracted from the BCI Competition IV EEG data set in a number of steps, i.e. normalization with the purpose of noise reduction using a median filter, feature extraction using a recurrent neural network, and classification using Twin Support Vector Machine. Then, a series of evaluation criteria were used to validate the proposed approach and compare it with similar methods. The results showed that the proposed approach has high accuracy

Noninvasive Neuroprosthetic Control of Grasping by Amputees

Smooth coordination and fine temporal control of muscles by the brain allows us to effortlessly pre-shape our hand to grasp different objects. Correlates of motor control for grasping have been found across wide-spread cortical areas, with diverse signal features. These signals have been harnessed by implanting intracortical electrodes and used to control the motion of robotic hands by tetraplegics, using algorithms called brain-machine interfaces (BMIs). Signatures of motor control signal encoding mechanisms of the brain in macro-scale signals such as electroencephalography (EEG) are unknown, and could potentially be used to develop noninvasive brain-machine interfaces. Here we show that a) low frequency (0.1 – 1 Hz) time domain EEG contains information about grasp pre-shaping in able-bodies individuals, and b) This information can be used to control pre-shaping motion of a robotic hand by amputees. In the first study, we recorded simultaneous EEG and hand kinematics as 5 able-bodies individuals grasped various objects. Linear decoders using low delta band EEG amplitudes accurately predicted hand pre-shaping kinematics during grasping. Correlation coefficient between predicted and actual kinematics was r = 0.59 ± 0.04, 0.47 ± 0.06 and 0.32 ± 0.05 for the first 3 synergies. In the second study, two transradial amputees (A1 and A2) controlled a prosthetic hand to grasp two objects using a closed-loop BMI with low delta band EEG. This study was conducted longitudinally in 12 sessions spread over 38 days. A1 achieved a 63% success rate, with 11 sessions significantly above chance. A2 achieved a 32% success rate, with 2 sessions significantly above chance. Previous methods of EEG-based BMIs used frequency domain features, and were thought to have a low signal-to-noise ratio making them unsuitable for control of dexterous tasks like grasping. Our results demonstrate that time-domain EEG contains information about grasp pre-shaping, which can be harnessed for neuroprosthetic control.Electrical and Computer Engineering, Department o

Non-Penetrating Microelectrode Interfaces for Cortical Neuroprosthetic Applications with a Focus on Sensory Encoding: Feasibility and Chronic Performance in Striate Cortex

abstract: Growing understanding of the neural code and how to speak it has allowed for notable advancements in neural prosthetics. With commercially-available implantable systems with bi- directional neural communication on the horizon, there is an increasing imperative to develop high resolution interfaces that can survive the environment and be well tolerated by the nervous system under chronic use. The sensory encoding aspect optimally interfaces at a scale sufficient to evoke perception but focal in nature to maximize resolution and evoke more complex and nuanced sensations. Microelectrode arrays can maintain high spatial density, operating on the scale of cortical columns, and can be either penetrating or non-penetrating. The non-penetrating subset sits on the tissue surface without puncturing the parenchyma and is known to engender minimal tissue response and less damage than the penetrating counterpart, improving long term viability in vivo. Provided non-penetrating microelectrodes can consistently evoke perception and maintain a localized region of activation, non-penetrating micro-electrodes may provide an ideal platform for a high performing neural prosthesis; this dissertation explores their functional capacity. The scale at which non-penetrating electrode arrays can interface with cortex is evaluated in the context of extracting useful information. Articulate movements were decoded from surface microelectrode electrodes, and additional spatial analysis revealed unique signal content despite dense electrode spacing. With a basis for data extraction established, the focus shifts towards the information encoding half of neural interfaces. Finite element modeling was used to compare tissue recruitment under surface stimulation across electrode scales. Results indicated charge density-based metrics provide a reasonable approximation for current levels required to evoke a visual sensation and showed tissue recruitment increases exponentially with electrode diameter. Micro-scale electrodes (0.1 – 0.3 mm diameter) could sufficiently activate layers II/III in a model tuned to striate cortex while maintaining focal radii of activated tissue. In vivo testing proceeded in a nonhuman primate model. Stimulation consistently evoked visual percepts at safe current thresholds. Tracking perception thresholds across one year reflected stable values within minimal fluctuation. Modulating waveform parameters was found useful in reducing charge requirements to evoke perception. Pulse frequency and phase asymmetry were each used to reduce thresholds, improve charge efficiency, lower charge per phase – charge density metrics associated with tissue damage. No impairments to photic perception were observed during the course of the study, suggesting limited tissue damage from array implantation or electrically induced neurotoxicity. The subject consistently identified stimulation on closely spaced electrodes (2 mm center-to-center) as separate percepts, indicating sub-visual degree discrete resolution may be feasible with this platform. Although continued testing is necessary, preliminary results supports epicortical microelectrode arrays as a stable platform for interfacing with neural tissue and a viable option for bi-directional BCI applications.Dissertation/ThesisDoctoral Dissertation Biomedical Engineering 201

Doctor of Philosophy

dissertationRecording the neural activity of human subjects is indispensable for fundamental neuroscience research and clinical applications. Human studies range from examining the neural activity of large regions of the cortex using electroencephalography (EEG) or electrocorticography (ECoG) to single neurons or small populations of neurons using microelectrode arrays. In this dissertation, microscale recordings in the human cortex were analyzed during administration of propofol anesthesia and articulate movements such as speech, finger flexion, and arm reach. Recordings were performed on epilepsy patients who required long-term electrocorticographic monitoring and were implanted with penetrating or surface microelectrode arrays. We used penetrating microelectrode arrays to investigate the effects of propofol anesthesia on action potentials (APs) and local field potentials (LFPs). Increased propofol concentration correlated with decreased high-frequency power in LFP spectra and decreased AP firing rates, as well as the generation of large amplitude spike-like LFP activity; however, the temporal relationship between APs and LFPs remained relatively consistent at all levels of propofol anesthesia. The propofol-induced suppression of neocortical network activity allowed LFPs to be dominated by low-frequency spike-like activity, and correlated with sedation and unconsciousness. As the low-frequency spike-like activity increased, and the AP-LFP relationship became more predictable, firing rate encoding capacity was impaired. This suggests a mechanism for decreased information processing in the neocortex that accounts for propofol-induced unconsciousness. We also demonstrated that speech, finger, and arm movements can be decoded from LFPs recorded with dense grids of microelectrodes placed on the surface of human cerebral cortex for brain computer interface (BCI) applications using LFPs recorded over face-motor area, vocalized articulations of ten different words and silence were classified on a trial-by-trial basis with 82.4% accuracy. Using LFPs recorded over the hand area of motor cortex, three individual finger movements and rest were classified on a trial-by-trial basis with 62% accuracy. LFPs recorded over the arm area of motor cortex were used to continuously decode the arm trajectory with a maximum correlation coefficient of 0.82 in the x-direction and 0.76 in the y-direction. These findings demonstrate that LFPs recorded by micro-ECoG grids from the surface of the cerebral cortex contain sufficient information to provide rapid and intuitive control a BCI communication or motor prosthesis

Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation

Sensing enabled implantable devices and next-generation neurotechnology allow real-time adjustments of invasive neuromodulation. The identification of symptom and disease-specific biomarkers in invasive brain signal recordings has inspired the idea of demand dependent adaptive deep brain stimulation (aDBS). Expanding the clinical utility of aDBS with machine learning may hold the potential for the next breakthrough in the therapeutic success of clinical brain computer interfaces. To this end, sophisticated machine learning algorithms optimized for decoding of brain states from neural time-series must be developed. To support this venture, this review summarizes the current state of machine learning studies for invasive neurophysiology. After a brief introduction to the machine learning terminology, the transformation of brain recordings into meaningful features for decoding of symptoms and behavior is described. Commonly used machine learning models are explained and analyzed from the perspective of utility for aDBS. This is followed by a critical review on good practices for training and testing to ensure conceptual and practical generalizability for real-time adaptation in clinical settings. Finally, first studies combining machine learning with aDBS are highlighted. This review takes a glimpse into the promising future of intelligent adaptive DBS (iDBS) and concludes by identifying four key ingredients on the road for successful clinical adoption: i) multidisciplinary research teams, ii) publicly available datasets, iii) open-source algorithmic solutions and iv) strong world-wide research collaborations.Fil: Merk, Timon. Charité – Universitätsmedizin Berlin; AlemaniaFil: Peterson, Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina. Harvard Medical School; Estados UnidosFil: Köhler, Richard. Charité – Universitätsmedizin Berlin; AlemaniaFil: Haufe, Stefan. Charité – Universitätsmedizin Berlin; AlemaniaFil: Richardson, R. Mark. Harvard Medical School; Estados UnidosFil: Neumann, Wolf Julian. Charité – Universitätsmedizin Berlin; Alemani

Review of the BCI Competition IV

The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They include high specialists as well as students. The goals of all BCI competitions have always been to challenge with respect to novel paradigms and complex data. We report on the following challenges: (1) asynchronous data, (2) synthetic, (3) multi-class continuous data, (4) session-to-session transfer, (5) directionally modulated MEG, (6) finger movements recorded by ECoG. As after past competitions, our hope is that winning entries may enhance the analysis methods of future BCIs.BMBF, 01IB001A, LOKI - Lernen zur Organisation komplexer Systeme der Informationsverarbeitung - Lernen im Kontext der SzenenanalyseBMBF, 01GQ0850, Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine InteraktionEC/FP7/224631/EU/Tools for Brain-Computer Interaction/TOBIEC/FP7/216886/EU/Pattern Analysis, Statistical Modelling and Computational Learning 2/PASCAL2BMBF, 01GQ0420, Verbundprojekt: Bernstein-Zentrum für Neural Dynamics, Freiburg - CNDFBMBF, 01GQ0761, Bewegungsassoziierte Aktivierung - Dekodierung bewegungsassoziierter GehirnsignaleBMBF, 01GQ0762, Bewegungsassoziierte Aktivierung - Gehirn- und Maschinenlerne