Paper defines interpretability for brain decoding models.
problem Lack of formal definition and quantitative measure for interpretability in brain decoding.
method Proposes a simple definition and combines interpretability with performance into a new criterion for model selection.
result Optimizing hyper-parameters based on the new criterion yields more informative linear models.
Paper defines and quantifies interpretability of brain decoding maps.
problem Difficulty in interpreting brain maps derived from multivariate classifiers.
method Theoretical definition of interpretability, decomposition into reproducibility and representativeness, heuristic method for approximating interpretability, multi-objective criterion for model selection.
result Optimizing hyper-parameters based on proposed criterion yields more informative brain maps.
New method aligns brain data across individuals for better brain decoding.
problem Inter-individual variability in brain response patterns limits decoder generalization.
method SpectralOT method that embeds cortical geometry into Laplace-Beltrami eigenmodes.
result SpectralOT strikes balance between aligning functional features and preserving anatomical structure.
Deep invertible networks decode EEG signals better than chance.
problem Decoding brain signals from EEG data.
method Deep invertible networks for generating and classifying brain signals.
result Deep invertible networks generate realistic EEG signals and classify novel signals above chance.
As a technology to read brain states from measurable brain activities, brain decoding are widely applied in industries and medical sciences. In spite of high demands in these applications for a universal decoder that can be applied to all individuals simultaneously, large variation in brain activities across individual…
New method combines brain imaging data from multiple studies to improve cognitive decoding.
problem Low statistical power in individual neuroimaging studies.
method A new methodology to analyze brain responses across tasks without a unified theoretical framework.
result Improves decoding performance for 80% of 35 functional-imaging studies.
Social-sparsity brain decoders improve speed and interpretability.
problem Computational cost and interpretability in brain decoding models.
method Introduced social-sparsity, a structured shrinkage operator.
result Social-sparsity performs almost as well as total-variation models and better than graph-net, with a fraction of the computational cost.
DI-SVM improves brain condition decoding performance via domain independence.
problem Transfer learning in brain imaging data with large p and small n.
method DI-SVM minimizes domain dependence via HSIC to learn common features.
result DI-SVM outperforms eight competing methods on brain decoding tasks.
This paper reviews cross-validation methods for brain decoding in neuroimaging.
problem Evaluating and tuning brain decoding models using cross-validation.
method Reviewed cross-validation procedures, including leave-one-out and repeated random splits, and discussed nested cross-validation.
result Popular leave-one-out method leads to unstable and biased estimates; repeated random splits are preferred.
Brain2Char decodes text from brain recordings, achieving state-of-the-art performance.
problem Directly decoding text from brain recordings for communication.
method Combines 3D Inception layers, bidirectional recurrent layers, and language model weighted beam search. Uses CTC loss and auxiliary losses for regularization.
result Achieves 10.6%, 8.5%, and 7.0% WER on vocabulary sizes from 1200 to 1900 words.
APA improves brain decoding accuracy using fMRI.
problem Decoding human brain patterns from fMRI images.
method APA combines anatomical feature extraction and AdaBoost for binary and multi-class predictions.
result APA outperforms existing methods in decoding visual stimuli.
APA improves brain decoding accuracy for visual stimuli.
problem Decoding patterns in human brain using MVPA.
method Developed novel anatomical feature extraction and AdaBoost algorithm.
result Superior performance in decoding visual stimuli categories.
CNN outperforms traditional methods in decoding brain states from MEG signals.
problem Classifying brain states from MEG signals using traditional methods.
method Generative model-based CNN optimized for MEG brain signals.
result The CNN outperforms more complex neural networks and traditional classifiers in decoding event-related responses and oscillatory brain activity.
Study uses simulation-based inference to decode brain activity from synthetic stimuli.
problem Reversing the process of brain activity emulation to recover stimuli or their properties.
method Pairing brain emulator with LLMs to learn a probabilistic mapping from brain maps to stimulus parameters.
result LLMs can serve as controllable stimulus generators and parameters can be recovered from brain maps.
A hybrid approach links fMRI data to deep features for visual category decoding.
problem Lack of practical fMRI decoder with CNN structure due to limited brain data.
method Kernel Canonical Correlation Analysis linking fMRI and deep learnt representations.
result Effective in distinguishing semantic visual categories using only brain imaging data.
New BMI decoder robust to future neural variability.
problem Current BMIs become ineffective with changing neural conditions.
method Trained multiplicative recurrent neural network to handle diverse neural conditions and synthetic perturbations.
result Successfully learned and became more robust to various neural-to-kinematic mappings.
New method reconstructs brain stimuli from responses.
problem Reconstructing perceived stimuli from brain responses.
method Combining probabilistic inference and adversarial training of neural networks.
result Generates state-of-the-art reconstructions of perceived faces.
Brain decoding involves the determination of a subject's cognitive state or an associated stimulus from functional neuroimaging data measuring brain activity. In this setting the cognitive state is typically characterized by an element of a finite set, and the neuroimaging data comprise voluminous amounts of spatiotemp…
DyEnsemble improves BCI accuracy by adapting to nonstationary neural signals.
problem Nonstationary neural signals in BCI cause decoding errors.
method Dynamic ensemble modeling that learns and combines diverse models online.
result DyEnsemble outperforms Kalman filters, especially with noisy signals.
A new encoding framework predicts brain activity from visual stimuli and intrinsic brain connections.
problem Traditional encoding models ignore brain inner states, limiting their performance in natural image identification.
method Proposes a novel encoding framework combining external stimuli and brain inner states, using a forward encoding model and an inner state model.
result The framework achieves better performance on natural image identification from fMRI responses than traditional models.
Inferring the functional specificity of brain regions from functional Magnetic Resonance Images (fMRI) data is a challenging statistical problem. While the General Linear Model (GLM) remains the standard approach for brain mapping, supervised learning techniques (a.k.a.} decoding) have proven to be useful to capture mu…
Deep neural nets decode brain tasks from MEG data.
problem Classifying brain states from MEG data.
method Three deep neural network models with attention mechanisms.
result Attention mechanisms improve model generalization.
Graphs improve brain activity decoding from fMRI data.
problem Decoding brain activity from fMRI data.
method Dimensionality reduction techniques based on graph representations of the brain.
result Mixed graphs using both geometric structure and functional connectivity offer the best performance.
Machine learning improves neural decoding performance.
problem Traditional neural decoding methods are inefficient.
method Apply modern machine learning algorithms (neural networks, gradient boosting) for neural decoding.
result Modern methods significantly outperform traditional approaches.
Neuroprosthetic brain-computer interfaces function via an algorithm which decodes neural activity of the user into movements of an end effector, such as a cursor or robotic arm. In practice, the decoder is often learned by updating its parameters while the user performs a task. When the user's intention is not directly…
Survey of deep learning methods for fMRI natural image reconstruction.
problem Reconstructing natural images from fMRI brain activity.
method Survey of deep learning approaches, including architectural design, datasets, and evaluation metrics.
result Performance evaluation across standardized metrics.
DeepLight uses LSTM to decode fMRI data, improving cognitive state analysis.
problem Challenges in analyzing neuroimaging data due to high dimensionality, low sample size, and complex dependency structure.
method Introduces DeepLight framework using LSTM-based DL models to process fMRI data sequences, adapts LRP for interpretability.
result DeepLight outperforms conventional fMRI analysis methods in decoding cognitive states and identifying brain regions.
Deep neural networks decode natural visual scenes from neural spikes.
problem Decoding visual scenes from neural spikes for brain-machine interfaces.
method Developed a novel spike-image decoder (SID) using deep neural networks.
result SID reconstructs natural visual scenes from neural spikes with high accuracy.
New method labels EEG recordings for efficient neural decoding evaluation.
problem Challenges in evaluating neural decoding methods on limited, noisy data.
method Post-hoc labeling of arbitrary EEG recordings for generating labeled datasets.
result Generates large labeled datasets for benchmarking neural decoding methods.
Novel model for decoding visual stimuli in human brains.
problem Challenges in MVP techniques, including noise and sparsity, and the cost of brain studies.
method Automatic detection of active regions, new Gaussian smoothing method, combining fMRI data sets.
result Superior performance compared to state-of-the-art methods.
Deep learning improves error detection in intracranial EEG.
problem Improving error detection in intracranial EEG.
method Employed convolutional neural networks (CNNs) for classification and characterization of error-related brain responses.
result CNNs outperformed traditional methods in classifying and decoding errors in intracranial EEG.
Develops a new neural spike train decoding framework using topological data.
problem Decoding neural spike trains from head direction and grid cells.
method Combines simplicial complex discovery with deep learning to capture higher-order connectivity.
result Demonstrates effectiveness on head direction and trajectory prediction datasets.
While invasively recorded brain activity is known to provide detailed information on motor commands, it is an open question at what level of detail information about positions of body parts can be decoded from non-invasively acquired signals. In this work it is shown that index finger positions can be differentiated fr…
Neural network optimizes MRI pipeline for better brain activity analysis.
problem Inaccurate parameter setting across MRI pipeline modules.
method Converted pipeline into a deep neural network, jointly optimising parameters.
result Adaptive spatial smoothing module improves brain decoding accuracy.
Proposes new feature transformation methods for brain interface models.
problem Sub-optimality of feature ranking and selection in brain interface models.
method Introduces maximum mutual information linear and nonlinear transformations.
result Significantly better performance in binary and multi-class decoding analyses.
Tensor models decode human perception and memory using SPO triples.
problem Understanding implicit and explicit perception and memory in the brain.
method Tensor models with SPO triples, dual representations, and four layers.
result Semantic memory is crucial for explicit perception and declarative memories.
Sparse coding algorithms outperform ICA in classifying fMRI brain activity.
problem Classifying cognitive activity from fMRI data using different encoding methods.
method Comparison of Non-negative Matrix Factorization (NMF), Independent Component Analysis (ICA), and Spatial Sparse Coding algorithms.
result Spatial sparse coding algorithms, particularly L1 Regularized Learning, outperform ICA in classifying fMRI data. Deep transfer learning improves fMRI decoding from small datasets.
problem Small sample size and high dimensionality of fMRI datasets.
method Transfer learning using a pre-trained deep learning model on a large dataset.
result A pre-trained DL model outperforms a model trained from scratch on a new dataset.
Robot learns user preferences from brain signals.
problem Decoding user preferences for robot motions from brain signals.
method Proposes a novel approach using electroencephalography to decode user preferences from brain signals.
result Brain signals can reliably infer user preferences for robot trajectories.
Pattern recognition in neuroimaging distinguishes between two types of models: encoding- and decoding models. This distinction is based on the insight that brain state features, that are found to be relevant in an experimental paradigm, carry a different meaning in encoding- than in decoding models. In this paper, we a…
Brain decoding is a data analysis paradigm for neuroimaging experiments that is based on predicting the stimulus presented to the subject from the concurrent brain activity. In order to make inference at the group level, a straightforward but sometimes unsuccessful approach is to train a classifier on the trials of a g…
The functional and structural representation of the brain as a complex network is marked by the fact that the comparison of noisy and intrinsically correlated high-dimensional structures between experimental conditions or groups shuns typical mass univariate methods. Furthermore most network estimation methods cannot d…
Geometry-aware models improve cross-subject EEG decoding accuracy.
problem Strong inter-subject variability in motor imagery decoding.
method Discriminative Congruence Transform (DCT), Deep Linear DCT (DLDCT), Deep DCT-UNet (DDCT-UNet).
result Improves transductive cross-subject accuracy by 2-3%.
This paper introduces MOCM for better fMRI analysis stability.
problem Stable performance of MVP models on new fMRI datasets.
method Integrated objective function and multi-objective optimization approach.
result Superior performance compared to other techniques.
New test assesses shared brain activity across different cognitive modalities.
problem Determining if different cognitive modalities use overlapping neural representations.
method Formulated a statistical hypothesis testing approach using permutation testing.
result New test (CMPT) has greater statistical power than cross-modal decoding while maintaining low Type I errors.
A high-parallelism SNN improves feature learning efficiency and robustness.
problem Slow learning speed and limited learning capability in existing SNNs.
method Inspired by Inception modules, high-parallelism architecture, Vote-for-All decoding, adaptive repolarization mechanism.
result Superior performance and competitive accuracy compared to state-of-the-art unsupervised SNNs.
DKF uses nonlinear, Gaussian approximations for better neural decoding.
problem Improving neural decoding for brain-computer interfaces.
method Developed a Discriminative Kalman Filter (DKF) for nonlinear, non-Gaussian state estimation.
result DKF successfully enabled quadriplegic users to control devices using mental imagery.
Generative model predicts multiple brain graphs from one, preserving topology.
problem Predicting multiple brain graphs from a single one, preserving topology.
method MultiGraphGAN architecture, graph adversarial auto-encoder, cluster-specific decoders, topological loss.
result Significantly outperformed variants in multi-view brain graph generation.