Minimax-optimal decoding of movement goals from local field potentials using complex spectral features

Angjelichinoski, Marko; Banerjee, Taposh; Choi, John; Pesaran, Bijan; Tarokh, Vahid

doi:10.1088/1741-2552/ab1a1f

Cited by 10 publications

(61 citation statements)

References 21 publications

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“…We begin with a brief overview of the experimental setup, schematically depicted in Fig. 1 (see also [4], [6], [41] for further details). All experimental procedures were approved by the NYU University Animal Welfare Committee (UAWC).…”

Section: A Description Of the Experimentsmentioning

confidence: 99%

“…We give a brief, informal digest of the feature extraction technique based on the famous Pinsker's theorem; for more rigorous theoretical treatment of the theorem and related concepts such as Gaussian sequence models and minimax optimality, we advise the interested reader to refer to [42], [43] as well as [4], [5] where Pinsker's theorem was first applied for extracting features from noisy LFP signals.…”

Section: B Pinsker's Theorem For Extracting Features From Lfpsmentioning

confidence: 99%

“…The theory of non-parametric regression has proven to be crucial for the success of LFP-based decoders. As documented in [4], [5], the application of non-parametric regression to LFPs has led to the development of complex spectrum-based feature extraction technique based on the famous Pinsker's theorem. As opposed to popular feature extraction methods such as the conventional Power Spectrum Density (PSD)-based decoders [6], or decoders based on the spatial covariance matrices of the trials [7], [8] which take into account only the information stored in the LFP signal amplitude, Pinsker's feature extraction methodology naturally incorporates both the amplitude and the phase information inherently present in the LFP signals.…”

Section: Introductionmentioning

confidence: 99%

“…As opposed to popular feature extraction methods such as the conventional Power Spectrum Density (PSD)-based decoders [6], or decoders based on the spatial covariance matrices of the trials [7], [8] which take into account only the information stored in the LFP signal amplitude, Pinsker's feature extraction methodology naturally incorporates both the amplitude and the phase information inherently present in the LFP signals. As a result, even simple linear classification methods when applied over Pinsker's features exhibit significant performance improvements in decoding intended motor actions [4].…”

Section: Introductionmentioning

confidence: 99%

“…However, the studies cited above apply subject-specific training and apart from the preliminary work reported in [38], we are not familiar with other dedicated work on transfer learning for cross-subject decoding that uses electrophysiology such as LFPs.Our contributions. In this paper, we introduce novel supervised transfer learning method, tailored for cross-subject decoding of intended motor actions from LFP recordings in Pinsker's feature space [4]. We refer to the method as data centering; its pivotal idea is to adapt the feature space of the source subject where the training data is collected to the feature space of the…”

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confidence: 99%

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Cross-subject decoding of eye movement goals from local field potentials

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Objective. We consider the cross-subject decoding problem from local field potential (LFP) signals, where training data collected from the prefrontal cortex (PFC) of a source subject is used to decode intended motor actions in a destination subject. Approach. We propose a novel supervised transfer learning technique, referred to as data centering, which is used to adapt the feature space of the source to the feature space of the destination. The key ingredients of data centering are the transfer functions used to model the deterministic component of the relationship between the source and destination feature spaces. We propose an efficient data-driven estimation approach for linear transfer functions that uses the first and second order moments of the class-conditional distributions. Main result. We apply our data centering technique with linear transfer functions for cross-subject decoding of eye movement intentions in an experiment where two macaque monkeys perform memory-guided visual saccades to one of eight target locations. The results show peak cross-subject decoding performance of 80%, which marks a substantial improvement over random choice decoder. In addition to this, data centering also outperforms standard sampling-based methods in setups with imbalanced training data. Significance. The analyses presented herein demonstrate that the proposed data centering is a viable novel technique for reliable LFP-based cross-subject brain-computer interfacing and neural prostheses.Review of existing work. Despite the challenges imposed by the non-stationary nature of neuronal activity signals, the cross-subject decoding problem in non-invasive, EEG-based BCIs has been addressed and the progress has been reported. To this end, methods from the emerging field of transfer learning have been extensively applied, albeit to varying degrees of success [9]. In its most general, transfer learning refers to a set of data mining and machine learning techniques, procedures and algorithms designed to extrapolate knowledge acquired in a given domain and apply it in different but somewhat related domain [9], [15], [16]. The a priori assumption in transfer learning is that there exist inherent connections, correlations and/or similarities between the domains, and the objective of any transfer learning algorithm is to discover these similarity structures and foster reliable transfer of knowledge across the domains [15]. It should be also noted that transfer learning is often done in a non-parametric, data-driven manner without relying extensively on detailed statistical models; this has proven to be effective in problems characterized with highly non-stationary temporal and/or spatial behaviour.In the context of cross-subject BCIs, which can be seen as one specific example of transfer learning, several techniques for identifying and estimating structural similarities between neurological data collected in different subjects have emerged; they can be organized into several broader categories which we briefly outline and discuss ne...

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Section: A Description Of the Experimentsmentioning

confidence: 99%

Section: B Pinsker's Theorem For Extracting Features From Lfpsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Cross-subject decoding of eye movement goals from local field potentials

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The relationship between frequency content and representational dynamics in the decoding of neurophysiological data

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et al. 2022

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Human stereoEEG recordings reveal network dynamics of decision-making in a rule-switching task

et al. 2020

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The processing steps that lead up to a decision, i.e., the transformation of sensory evidence into motor output, are not fully understood. Here, we combine stereoEEG recordings from the human cortex, with single-lead and time-resolved decoding, using a wide range of temporal frequencies, to characterize decision processing during a rule-switching task. Our data reveal the contribution of rostral inferior parietal lobule (IPL) regions, in particular PFt, and the parietal opercular regions in decision processing and demonstrate that the network representing the decision is common to both task rules. We reconstruct the sequence in which regions engage in decision processing on single trials, thereby providing a detailed picture of the network dynamics involved in decision-making. The reconstructed timeline suggests that the supramarginal gyrus in IPL links decision regions in prefrontal cortex with premotor regions, where the motor plan for the response is elaborated.

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Minimax-optimal decoding of movement goals from local field potentials using complex spectral features

Cited by 10 publications

References 21 publications

Cross-subject decoding of eye movement goals from local field potentials

Cross-subject decoding of eye movement goals from local field potentials

The relationship between frequency content and representational dynamics in the decoding of neurophysiological data

Human stereoEEG recordings reveal network dynamics of decision-making in a rule-switching task

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