Decoding of object categories from brain signals using cross frequency coupling methods

Jafakesh, Sara; Jahromy, Fatemeh Zareayan; Daliri, Mohammad Reza

doi:10.1016/j.bspc.2016.01.013

Cited by 25 publications

(12 citation statements)

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“…PAC is believed to reflect neural coding of signals within the local microscale and macroscale networks of the brain (Canolty and Knight, 2010 ). There is increasing experimental evidence suggesting that PAC may provide more useful information for decoding of object categories (Watrous et al, 2015 ; Jafakesh et al, 2016 ), which need to be deeply studied in future once high quality data of EEG or ECoG recording is available.…”

Section: Discussionmentioning

confidence: 99%

Decoding English Alphabet Letters Using EEG Phase Information

Wang

2018

Front. Neurosci.

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Increasing evidence indicates that the phase pattern and power of the low frequency oscillations of brain electroencephalograms (EEG) contain significant information during the human cognition of sensory signals such as auditory and visual stimuli. Here, we investigate whether and how the letters of the alphabet can be directly decoded from EEG phase and power data. In addition, we investigate how different band oscillations contribute to the classification and determine the critical time periods. An English letter recognition task was assigned, and statistical analyses were conducted to decode the EEG signal corresponding to each letter visualized on a computer screen. We applied support vector machine (SVM) with gradient descent method to learn the potential features for classification. It was observed that the EEG phase signals have a higher decoding accuracy than the oscillation power information. Low-frequency theta and alpha oscillations have phase information with higher accuracy than do other bands. The decoding performance was best when the analysis period began from 180 to 380 ms after stimulus presentation, especially in the lateral occipital and posterior temporal scalp regions (PO7 and PO8). These results may provide a new approach for brain-computer interface techniques (BCI) and may deepen our understanding of EEG oscillations in cognition.

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Section: Discussionmentioning

confidence: 99%

Decoding English Alphabet Letters Using EEG Phase Information

Wang

2018

Front. Neurosci.

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“…Recent studies have developed lots of methods to classify or identify visual stimuli from evoked fMRI/EEG responses. These category-based visual perceptual decoding researches explored the perceived categories of natural images (Behroozi & Daliri, 2014;Behroozi, Daliri, & Shekarchi, 2015;Carlson, Schrater, & He, 2003;Cox & Savoy, 2003;Haxby et al, 2001;Jafakesh, Jahromy, & Daliri, 2016;Kamitani & Tong, 2005;Kay, Naselaris, Prenger, & Gallant, 2008;Song, Zhan, Long, Zhang, & Yao, 2011;Tafreshi, Daliri, & Ghodousi, 2019;Taghizadeh-Sarabi, Daliri, & Niksirat, 2015;Torabi, Zareayan Jahromy, & Daliri, 2017), real inner thoughts (Yang et al, 2014), imagined categories (Naselaris, Olman, Stansbury, Ugurbil, & Gallant, 2015) and categories occurring in dreams (Horikawa, Tamaki, Miyawaki, & Kamitani, 2013), and so on. These studies strove to improve the decoding performance of their research fields from different experimental protocols and methods.…”

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confidence: 99%

Long short‐term memory‐based neural decoding of object categories evoked by natural images

Huang

Yan

Wang

et al. 2020

Human Brain Mapping

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Visual perceptual decoding is one of the important and challenging topics in cognitive neuroscience. Building a mapping model between visual response signals and visual contents is the key point of decoding. Most previous studies used peak response signals to decode object categories. However, brain activities measured by functional magnetic resonance imaging are a dynamic process with time dependence, so peak signals cannot fully represent the whole process, which may affect the performance of decoding. Here, we propose a decoding model based on long short‐term memory (LSTM) network to decode five object categories from multitime response signals evoked by natural images. Experimental results show that the average decoding accuracy using the multitime (2–6 s) response signals is 0.540 from the five subjects, which is significantly higher than that using the peak ones (6 s; accuracy: 0.492; p < .05). In addition, from the perspective of different durations, methods and visual areas, the decoding performances of the five object categories are deeply and comprehensively explored. The analysis of different durations and decoding methods reveals that the LSTM‐based decoding model with sequence simulation ability can fit the time dependence of the multitime visual response signals to achieve higher decoding performance. The comparative analysis of different visual areas demonstrates that the higher visual cortex (VC) contains more semantic category information needed for visual perceptual decoding than lower VC.

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“…CFC can describe the coordination between different frequency components of LFPs. Indeed, the PAC of the amplitude of high-frequency activity to the phase of slower oscillations has been described both in animals and in humans during different cognitive tasks (Axmacher et al 2010;Jafakesh et al 2016). In this study, the modulation between theta phase and gamma amplitude of multi-channel LFPs in the medial PFC and ventral HPC was calculated by using the PAC algorithm.…”

Section: Cfc Analysismentioning

confidence: 99%

Information interactions between prefrontal cortex and hippocampus during performance of a spatial working memory task

Zhang

Guo

Liu

et al. 2021

Preprint

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Spatial working memory (SWM) refers to a short-term system for temporary manipulation of spatial information and requires the cooperation of multiple brain regions. Despite evidence that hippocampus (HPC) and prefrontal cortex (PFC) are involved in SWM, how PFC and HPC coordinate the neural information during SWM remains puzzling. In this study, local field potentials (LFPs) were recorded simultaneously from rat ventral HPC and medial PFC during SWM tasks. Then cross-frequency coupling algorithm was used as functional connectivity for construction of undirected networks; Grange causality algorithm was used as effective connectivity for construction of directed networks. Finally, information interactions across two brain regions were analyzed based on undirected and directed networks. Experimental results show that LFPs power in PFC and HPC both decreased over learning days and peaked before the reference point during SWM, moreover, LFPs mainly distributed in theta and gamma. From the undirected aspect, undirected PFC subnetwork and HPC subnetwork have the same effect on information transmission for SWM; the PAC between PFC-gamma and HPC-theta in undirected PFC-HPC network is related to SWM formation and contributes to information interactions between PFC and HPC. From the directed aspect, the effect of information transmission in directed HPC subnetwork is greater than PFC subnetwork; the enhancement of coordination between directed PFC and HPC subnetworks contributes to correct execution of SWM tasks; directed HPC→PFC network plays a predominant role in information interaction; with the increasing of learning days, PFC and HPC tend to be the causal sink and causal source of information flow.

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Decoding of object categories from brain signals using cross frequency coupling methods

Cited by 25 publications

References 50 publications

Decoding English Alphabet Letters Using EEG Phase Information

Decoding English Alphabet Letters Using EEG Phase Information

Long short‐term memory‐based neural decoding of object categories evoked by natural images

Information interactions between prefrontal cortex and hippocampus during performance of a spatial working memory task

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