SpikeDeeptector: A deep-learning based method for detection of neural spiking activity

Objective. In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does not record any neural data, but only noise. By noise, we mean physiological activities unrelated to spiking, including technical artifacts and neural activities of neurons that are too far away from the electrode to be usefully processed. For further analysis, an automatic identification and continuous tracking of channels containing neural data is of great significance for many applications, e.g. automated selection of neural channels during online and offline spike sorting. Automated spike detection and sorting is also critical for online decoding in brain–computer interface (BCI) applications, in which only simple threshold crossing events are often considered for feature extraction. To our knowledge, there is no method that can universally and automatically identify channels containing neural data. In this study, we aim to identify and track channels containing neural data from implanted electrodes, automatically and more importantly universally. By universally, we mean across different recording technologies, different subjects and different brain areas. Approach. We propose a novel algorithm based on a new way of feature vector extraction and a deep learning method, which we call SpikeDeeptector. SpikeDeeptector considers a batch of waveforms to construct a single feature vector and enables contextual learning. The feature vectors are then fed to a deep learning method, which learns contextualized, temporal and spatial patterns, and classifies them as channels containing neural spike data or only noise. Main results. We trained the model of SpikeDeeptector on data recorded from a single tetraplegic patient with two Utah arrays implanted in different areas of the brain. The trained model was then evaluated on data collected from six epileptic patients implanted with depth electrodes, unseen data from the tetraplegic patient and data from another tetraplegic patient implanted with two Utah arrays. The cumulative evaluation accuracy was 97.20% on 1.56 million hand labeled test inputs. Significance. The results demonstrate that SpikeDeeptector generalizes not only to the new data, but also to different brain areas, subjects, and electrode types not used for training. Clinical trial registration number. The clinical trial registration number for patients implanted with the Utah array is NCT 01849822. For the epilepsy patients, approval from the local ethics committee at the Ruhr-University Bochum, Germany, was obtained prior to implantation

SpikeDeep-classifier: a deep-learning based fully automatic offline spike sorting algorithm

Objective. Advancements in electrode design have resulted in micro-electrode arrays with hundreds of channels for single cell recordings. In the resulting electrophysiological recordings, each implanted electrode can record spike activity (SA) of one or more neurons along with background activity (BA). The aim of this study is to isolate SA of each neural source. This process is called spike sorting or spike classification. Advanced spike sorting algorithms are time consuming because of the human intervention at various stages of the pipeline. Current approaches lack generalization because the values of hyperparameters are not fixed, even for multiple recording sessions of the same subject. In this study, a fully automatic spike sorting algorithm called "SpikeDeep-Classifier" is proposed. The values of hyperparameters remain fixed for all the evaluation data. Approach. The proposed approach is based on our previous study (SpikeDeeptector) and a novel background activity rejector (BAR), which are both supervised learning algorithms and an unsupervised learning algorithm (K-means). SpikeDeeptector and BAR are used to extract meaningful channels and remove BA from the extracted meaningful channels, respectively. The process of clustering becomes straight-forward once the BA is completely removed from the data. Then, K-means with a predefined maximum number of clusters is applied on the remaining data originating from neural sources only. Lastly, a similarity-based criterion and a threshold are used to keep distinct clusters and merge similar looking clusters. The proposed approach is called cluster accept or merge (CAOM) and it has only two hyperparameters (maximum number of clusters and similarity threshold) which are kept fixed for all the evaluation data after tuning. Main Results. We compared the results of our algorithm with ground-truth labels. The algorithm is evaluated on data of human patients and publicly available labeled non-human primates (NHPs) datasets. The average accuracy of BAR on datasets of human patients is 92.3% which is further reduced to 88.03% after (K-means + CAOM). In addition, the average accuracy of BAR on a publicly available labeled dataset of NHPs is 95.40% which reduces to 86.95% after (K-mean + CAOM). Lastly, we compared the performance of the SpikeDeep-Classifier with two human experts, where SpikeDeep-Classifier has produced comparable results. Significance. The results demonstrate that "SpikeDeep-Classifier" possesses the ability to generalize well on a versatile dataset and henceforth provides a generalized well on a versatile dataset and henceforth provides a generalized and fully automated solution to offline spike sorting

SpikeDeeptector: A deep-learning based method for detection of neural spiking activity

Objective. In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does not record any neural data, but only noise. By noise, we mean physiological activities unrelated to spiking, including technical artifacts and neural activities of neurons that are too far away from the electrode to be usefully processed. For further analysis, an automatic identification and continuous tracking of channels containing neural data is of great significance for many applications, e.g. automated selection of neural channels during online and offline spike sorting. Automated spike detection and sorting is also critical for online decoding in brain–computer interface (BCI) applications, in which only simple threshold crossing events are often considered for feature extraction. To our knowledge, there is no method that can universally and automatically identify channels containing neural data. In this study, we aim to identify and track channels containing neural data from implanted electrodes, automatically and more importantly universally. By universally, we mean across different recording technologies, different subjects and different brain areas. Approach. We propose a novel algorithm based on a new way of feature vector extraction and a deep learning method, which we call SpikeDeeptector. SpikeDeeptector considers a batch of waveforms to construct a single feature vector and enables contextual learning. The feature vectors are then fed to a deep learning method, which learns contextualized, temporal and spatial patterns, and classifies them as channels containing neural spike data or only noise. Main results. We trained the model of SpikeDeeptector on data recorded from a single tetraplegic patient with two Utah arrays implanted in different areas of the brain. The trained model was then evaluated on data collected from six epileptic patients implanted with depth electrodes, unseen data from the tetraplegic patient and data from another tetraplegic patient implanted with two Utah arrays. The cumulative evaluation accuracy was 97.20% on 1.56 million hand labeled test inputs. Significance. The results demonstrate that SpikeDeeptector generalizes not only to the new data, but also to different brain areas, subjects, and electrode types not used for training. Clinical trial registration number. The clinical trial registration number for patients implanted with the Utah array is NCT 01849822. For the epilepsy patients, approval from the local ethics committee at the Ruhr-University Bochum, Germany, was obtained prior to implantation

Assessing current challenges in invasive brain-computer interface research

Brain-Computer Interfaces (BCIs) nutzen neuronale Daten und Dekodierungsalgorithmen um Bewegungsintentionen aus aufgenommen Hirnsignalen zu extrahieren. Die extrahierte Bewegungsintention wird dann von einem End-effektor, zum Beispiel einem Roboterarm, ausgeführt. BCIs können auf diese Weise schwerst gelähmten Patienten helfen Autonomie zurückzugewinnen und ihre Lebensqualität signifikant zu verbessern. Die motorischen Fähigkeiten von Patienten, die ein BCI benutzen, sind jedoch immer noch weit entfernt von den Fähigkeiten gesunder Menschen. Diesen Unterschied zu minimieren ist das Hauptziel der BCI-Forschung. Diese Dissertation wird einen Überblick über die Faktoren bieten, die zu diesem Unterschied beitragen und Ergebnisse aus Experimenten präsentieren, deren Ziel es ist BCI-Systeme zu verbessern.Brain-Computer Interfaces (BCI) use neural data and decoding algorithms to extract intentions from recorded brain signals. These intentions are then carried out by an end-effector device, for example a robotic arm. BCIs can help severely paralyzed patients regain some autonomy and improve their quality of life significantly. However, the motor performance that patients can achieve using state-of-the-art BCIs is still far from what healthy people would achieve. Minimizing this difference is the key objective of BCI research, and this thesis will provide an overview about the factors that contribute to this difference and present results from experiment that can improve these systems

Quantifying the alignment error and the effect of incomplete somatosensory feedback on motor performance in a virtual brain–computer-interface setup

Invasive brain–computer-interfaces (BCIs) aim to improve severely paralyzed patient’s (e.g. tetraplegics) quality of life by using decoded movement intentions to let them interact with robotic limbs. We argue that the performance in controlling an end-effector using a BCI depends on three major factors: decoding error, missing somatosensory feedback and alignment error caused by translation and/or rotation of the end-effector relative to the real or perceived body. Using a virtual reality (VR) model of an ideal BCI decoder with healthy participants, we found that a significant performance loss might be attributed solely to the alignment error. We used a shape-drawing task to investigate and quantify the effects of robot arm misalignment on motor performance independent from the other error sources. We found that a 90° rotation of the robot arm relative to the participant leads to the worst performance, while we did not find a significant difference between a 45° rotation and no rotation. Additionally, we compared a group of subjects with indirect haptic feedback with a group without indirect haptic feedback to investigate the feedback-error. In the group without feedback, we found a significant difference in performance only when no rotation was applied to the robot arm, supporting that a form of haptic feedback is another important factor to be considered in BCI control