An Ultralow-Power Sleep Spindle Detection System on Chip

Iranmanesh, Saam; Rodriguez‐Villegas, Esther

doi:10.1109/tbcas.2017.2690908

Cited by 16 publications

(8 citation statements)

References 25 publications

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“…SEF is a common feature used in monitoring the depth of anesthesia and stages of sleep using EEG (Iranmanesh and Rodriguez-Villegas, 2017). It measures the frequency which covers X percent of the PSD.…”

Section: Spectral Edge Frequency (Sef 95%)mentioning

confidence: 99%

Temporal variabilities provide additional category-related information in object category decoding: a systematic comparison of informative EEG features

Karimi-Rouzbahani

2020

Preprint

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Humans are remarkably efficent at recognizing objects. Understanding how the brain performs object recognition has been challenging. Our understanding has been advanced substantially in recent years with the development of multivariate pattern analysis or brain decoding methods. Most start-of-the-art decoding procedures, make use of the mean signal activation to extract object category information, which overlooks temporal variability in the signals. Here, we studied category-related information in 30 mathematically different features from the electroencephalography (EEG; the largest set ever) across three independent and highly-varied datasets using multi-variate pattern analyses. While the event-related potential (ERP) components of N1 and P2a were among the most informative features, the informative original signal samples and Wavelet coefficients, down-sampled through principal component analysis, outperformed them. Informative features showed more pronounced effects in the Theta frequency band, which has been shown to support feed-forward processing of visual information. Correlational analyses showed that the features which provided the most information about object categories, could predict participants’ performance (reaction time) more accurately than the less informative features. These results provide researchers with new avenues to study how the brain encodes object category information and how we can read out object category to study the temporal dynamics of the neural code.

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Section: Spectral Edge Frequency (Sef 95%)mentioning

confidence: 99%

Temporal variabilities provide additional category-related information in object category decoding: a systematic comparison of informative EEG features

Karimi-Rouzbahani

2020

Preprint

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“…Median frequency is the median normalized frequency of the power spectrum of the signal and the average frequency is the number of times the signal time series crosses zero. They have shown information about visual categories in previous studies (Iranmanesh and Rodriguez-Villegas, 2017;Joshi et al, 2018;Jadidi et al, 2016).…”

Section: Mean Median and Average Frequency (Mean Freq Med Freq And Avg Freq)mentioning

confidence: 84%

When the whole is less than the sum of its parts: maximum object category information and behavioral prediction in multiscale activation patterns

Karimi-Rouzbahani

Woolgar

2021

Preprint

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Neural codes are reflected in complex, temporally and spatially specific patterns of activation. One popular approach to decode neural codes in electroencephalography (EEG) is multivariate decoding. This approach examines the discriminability of activity patterns across experimental conditions to test if EEG contains information about those conditions. However, conventional decoding analyses ignore aspects of neural activity which are informative. Specifically, EEG data can be decomposed into a large number of mathematically distinct features (e.g., entropy, Fourier and Wavelet coefficients) which can reflect different aspects of neural activity. We previously compared 30 such features of EEG data, and found that visual category, and participant behavior, can be more accurately predicted using multiscale spatiotemporally sensitive Wavelet coefficients than mean amplitude (Karimi-Rouzbahani et al., 2021b). Here, we considered that even this larger set of features may only partially capture the underlying neural code, because the brain could use a combination of encoding protocols within a single trial which is not reflected in any one mathematical feature alone. To check, we combined those mathematical features using state-of-the-art supervised and unsupervised feature selection procedures (n = 17). Across 3 datasets, we compared decoding of visual object category between these 17 sets of combined features, and between combined and individual features. Object category could be robustly decoded using the combined features from all of the 17 algorithms. However, the combination of features, which were equalized in dimension to the individual features, were outperformed in most of the time points by the most informative individual feature (Wavelet coefficients). Moreover, the Wavelet coefficients also explained the behavioral performance more accurately than the combined features. These results suggest that a single but multiscale encoding protocol may capture the neural code better than any combination of features. Our findings put new constraints on the models of neural information encoding in EEG.

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“…Specifically, as data transformation from time to frequency domain is almost lossless using Fourier transform, oscillatory power basically reflects frequency-specific variance (with the total power reflecting the overall variance of the time series; Waschke et al, 2021). Motivated by previous studies showing signatures of object categories in the frequency domain (Behroozi et al, 2016;Rupp et al, 2017;Iranmanesh & Rodriguez-Villegas, 2017;Joshi et al, 2018;Jadidi et al, 2016) and the representation of temporal codes of visual information in the frequency domain (Eckhorn et al, 1988), we also extracted frequency-domain features to see if they could provide additional category-related information to time-domain features. It is of note that while the whole-trial analysis allows us to compare our results with previous studies, the evoked EEG potentials are generally nonstationary (i.e., their statistical properties change along the trial) and potentially dominated by low-frequency components.…”

Section: Frequency-domain Featuresmentioning

confidence: 99%

“…Spectral edge frequency (SEF 95%). This is a common feature used in monitoring the depth of anesthesia and stages of sleep using EEG (Iranmanesh & Rodriguez-Villegas, 2017). It measures the frequency that covers X percent of the PSD.…”

Section: Frequency-domain Featuresmentioning

confidence: 99%

Temporal Variabilities Provide Additional Category-Related Information in Object Category Decoding: A Systematic Comparison of Informative EEG Features

et al. 2021

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How does the human brain encode visual object categories? Our understanding of this has advanced substantially with the development of multivariate decoding analyses. However, conventional electroencephalography (EEG) decoding predominantly uses the mean neural activation within the analysis window to extract category information. Such temporal averaging overlooks the within-trial neural variability that is suggested to provide an additional channel for the encoding of information about the complexity and uncertainty of the sensory input. The richness of temporal variabilities, however, has not been systematically compared with the conventional mean activity. Here we compare the information content of 31 variability-sensitive features against the mean of activity, using three independent highly varied data sets. In whole-trial decoding, the classical event-related potential (ERP) components of P2a and P2b provided information comparable to those provided by original magnitude data (OMD) and wavelet coefficients (WC), the two most informative variability-sensitive features. In time-resolved decoding, the OMD and WC outperformed all the other features (including the mean), which were sensitive to limited and specific aspects of temporal variabilities, such as their phase or frequency. The information was more pronounced in the theta frequency band, previously suggested to support feedforward visual processing. We concluded that the brain might encode the information in multiple aspects of neural variabilities simultaneously such as phase, amplitude, and frequency rather than mean per se. In our active categorization data set, we found that more effective decoding of the neural codes corresponds to better prediction of behavioral performance. Therefore, the incorporation of temporal variabilities in time-resolved decoding can provide additional category information and improved prediction of behavior.

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An Ultralow-Power Sleep Spindle Detection System on Chip

Cited by 16 publications

References 25 publications

Temporal variabilities provide additional category-related information in object category decoding: a systematic comparison of informative EEG features

Temporal variabilities provide additional category-related information in object category decoding: a systematic comparison of informative EEG features

When the whole is less than the sum of its parts: maximum object category information and behavioral prediction in multiscale activation patterns

Temporal Variabilities Provide Additional Category-Related Information in Object Category Decoding: A Systematic Comparison of Informative EEG Features

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