1,721,056 research outputs found
Binarization Methods for Motor-Imagery Brain–Computer Interface Classification
Full text linkSuccessful motor-imagery brain-computer interface (MI-BCI) algorithms either extract a large number of handcrafted features and train a classifier, or combine feature extraction and classification within deep convolutional neural networks (CNNs). Both approaches typically result in a set of real-valued weights, that pose challenges when targeting real-time execution on tightly resource-constrained devices. We propose methods for each of these approaches that allow transforming real-valued weights to binary numbers for efficient inference. Our first method, based on sparse bipolar random projection, projects a large number of real-valued Riemannian covariance features to a binary space, where a linear SVM classifier can be learned with binary weights too. By tuning the dimension of the binary embedding, we achieve almost the same accuracy in 4-class MI (<= 1.27% lower) compared to models with float16 weights, yet delivering a more compact model with simpler operations to execute. Second, we propose to use memory-augmented neural networks (MANNs) for MI-BCI such that the augmented memory is binarized. Our method replaces the fully connected layer of CNNs with a binary augmented memory using bipolar random projection, or learned projection. Our experimental results on EEGNet, an already compact CNN for MI-BCI, show that it can be compressed by 1.28x at iso-accuracy using the random projection. On the other hand, using the learned projection provides 3.89% higher accuracy but increases the memory size by 28.10x
Hyperdimensional Computing with Local Binary Patterns: One-shot Learning for Seizure Onset Detection and Identification of Ictogenic Brain Regions from Short-time iEEG Recordings.
Full text linkOBJECTIVE
We develop a fast learning algorithm combining symbolic dynamics and brain-inspired hyperdimensional computing for both seizure onset detection and identification of ictogenic (seizure generating) brain regions from intracranial electroencephalography (iEEG).
METHODS
Our algorithm first transforms iEEG time series from each electrode into symbolic local binary pattern codes from which a holographic distributed representation of the brain state of interest is constructed across all the electrodes and over time in a hyperdimensional space. The representation is used to quickly learn from few seizures, detect their onset, and identify the spatial brain regions that generated them.
RESULTS
We assess our algorithm on our dataset that contains 99 short-time iEEG recordings from 16 drug-resistant epilepsy patients being implanted with 36 to 100 electrodes. For the majority of the patients (10 out of 16), our algorithm quickly learns from one or two seizures and perfectly (100%) generalizes on novel seizures using k-fold cross-validation. For the remaining six patients, the algorithm requires three to six seizures for learning. Our algorithm surpasses the state-of-the-art including deep learning algorithms by achieving higher specificity (94.84% vs. 94.77%) and macroaveraging accuracy (95.42% vs. 94.96%), and 74x lower memory footprint, but slightly higher average latency in detection (15.9 s vs. 14.7 s). Moreover, the algorithm can reliably identify (with a p-value < 0.01) the relevant electrodes covering an ictogenic brain region at two levels of granularity: cerebral hemispheres and lobes.
CONCLUSION AND SIGNIFICANCE
Our algorithm provides: (1) a unified method for both learning and classification tasks with end-to-end binary operations; (2) one-shot learning from seizure examples; (3) linear computational scalability for increasing number of electrodes; (4) generation of transparent codes that enables post-translational supports for clinical decision making
Laelaps: An Energy-Efficient Seizure Detection Algorithm from Long-term Human iEEG Recordings without False Alarms
Full text linkWe propose Laelaps, an energy-efficient and fast learning algorithm with no false alarms for epileptic seizure detection from long-term intracranial electroencephalography (iEEG) signals. Laelaps uses end-to-end binary operations by exploiting symbolic dynamics and brain-inspired hyperdimensional computing. Laelaps's results surpass those yielded by state-of-the-art (SoA) methods [1], [2], [3], including deep learning, on a new very large dataset containing 116 seizures of 18 drug-resistant epilepsy patients in 2656 hours of recordings each patient implanted with 24 to 128 iEEG electrodes. Laelaps trains 18 patient-specific models by using only 24 seizures: 12 models are trained with one seizure per patient, the others with two seizures. The trained models detect 79 out of 92 unseen seizures without any false alarms across all the patients as a big step forward in practical seizure detection. Importantly, a simple implementation of Laelaps on the Nvidia Tegra X2 embedded device achieves 1.7x 3.9 x faster execution and 1.4 x 2.9x lower energy consumption compared to the best result from the SoA methods. Our source code and anonymized iEEG dataset are freely available at http://ieeg-swez.ethz.ch
A neuro-vector-symbolic architecture for solving Raven’s progressive matrices
Full text linkNeither deep neural networks nor symbolic artificial intelligence (AI) alone has approached the kind of intelligence expressed in humans. This is mainly because neural networks are not able to decompose joint representations to obtain distinct objects (the so-called binding problem), while symbolic AI suffers from exhaustive rule searches, among other problems. These two problems are still pronounced in neuro-symbolic AI, which aims to combine the best of the two paradigms. Here we show that the two problems can be addressed with our proposed neuro-vector-symbolic architecture (NVSA) by exploiting its powerful operators on high-dimensional distributed representations that serve as a common language between neural networks and symbolic AI. The efficacy of NVSA is demonstrated by solving Raven's progressive matrices datasets. Compared with state-of-the-art deep neural network and neuro-symbolic approaches, end-to-end training of NVSA achieves a new record of 87.7% average accuracy in RAVEN, and 88.1% in I-RAVEN datasets. Moreover, compared with the symbolic reasoning within the neuro-symbolic approaches, the probabilistic reasoning of NVSA with less expensive operations on the distributed representations is two orders of magnitude faster.Neuro-symbolic artificial intelligence approaches display both perception and reasoning capabilities, but inherit the limitations of their individual deep learning and symbolic artificial intelligence components. By combining neural networks and vector-symbolic architectures, Hersche and colleagues propose a neuro-vector-symbolic framework that can solve Raven's progressive matrices tests faster and more accurately than other state-of-the-art methods
From variability tolerance to approximate computing in parallel integrated architectures and accelerators
No full textThis book focuses on computing devices and their design at various levels to combat variability. The authors provide a review of key concepts with particular emphasis on timing errors caused by various variability sources. They discuss methods to predict and prevent, detect and correct, and finally conditions under which such errors can be accepted; they also consider their implications on cost, performance and quality. Coverage includes a comparative evaluation of methods for deployment across various layers of the system from circuits, architecture, to application software. These can be combined in various ways to achieve specific goals related to observability and controllability of the variability effects, providing means to achieve cross layer or hybrid resilience. · Covers challenges and opportunities in identifying microelectronic variability and the resulting errors at various layers in the system abstraction; · Enables readers to assess how various levels of circuit and system design can mitigate the effects of variability; · Demonstrates overall system architecture of what is now called “approximate computing” paradigm in massively parallel integrated architectures and accelerators
One-shot Learning for iEEG Seizure Detection Using End-to-end Binary Operations: Local Binary Patterns with Hyperdimensional Computing
Full text linkThis paper presents an efficient binarized algorithm for both learning and classification of human epileptic seizures from intracranial electroencephalography (iEEG). The algorithm combines local binary patterns with brain-inspired hyperdimensional computing to enable end-to-end learning and inference with binary operations. The algorithm first transforms iEEG time series from each electrode into local binary pattern codes. Then atomic high-dimensional binary vectors are used to construct composite representations of seizures across all electrodes. For the majority of our patients (10 out of 16), the algorithm quickly learns from one or two seizures (i.e., one-/few-shot learning) and perfectly generalizes on 27 further seizures. For other patients, the algorithm requires three to six seizures for learning. Overall, our algorithm surpasses the state-of-the-art methods [1] for detecting 65 novel seizures with higher specificity and sensitivity, and lower memory footprint
In-memory factorization of holographic perceptual representations
No full textThis repository contains the manuscript of the work titled "In-memory factorization of holographic perceptual representations" which appeared in Nature Nanotechnology 30th March 2023
An Ensemble of Hyperdimensional Classifiers: Hardware-Friendly Short-Latency Seizure Detection with Automatic iEEG Electrode Selection.
Full text linkWe propose an intracranial electroencephalography (iEEG) based algorithm for detecting epileptic seizures with short latency, and with identifying the most relevant electrodes. Our algorithm first extracts three features, namely mean amplitude, line length, and local binary patterns that are fed to an ensemble of classifiers using hyperdimensional (HD) computing. These features are embedded into an HD space where well-defined vector-space operations are used to construct prototype vectors representing ictal (during seizures) and interictal (between seizures) brain states. Prototype vectors can be computed at different spatial scales ranging from a single electrode up to many electrodes covering different brain regions. This flexibility allows our algorithm to identify the iEEG electrodes that discriminate best between ictal and interictal brain states. We assess our algorithm on the SWEC-ETHZ iEEG dataset that includes 99 short-time iEEG seizures recorded with 36 to 100 electrodes from 16 drug-resistant epilepsy patients. Using k-fold cross-validation and all electrodes, our algorithm surpasses state-of-the-art algorithms yielding significantly shorter latency (8.81 s vs. 9.94 s) in seizure onset detection, and higher sensitivity (96.38 % vs. 92.72 %) and accuracy (96.85 % vs. 95.43 %). We can further reduce the latency of our algorithm to 3.74 s by allowing a slightly higher percentage of false alarms (2 % specificity loss). Using only the top 10 % of the electrodes ranked by our algorithm, we still maintain superior latency, sensitivity, and specificity compared to the other algorithms with all the electrodes. We finally demonstrate the suitability of our algorithm to deployment on low-cost embedded hardware platforms, thanks to its robustness to noise/artifacts affecting the signal, its low computational complexity, and the small memory-footprint on a RISC-V microcontroller
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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