A phase lag index hardware calculation for real-time electroencephalography studies

Nuno, Rafael Angel Gutierrez; Maharatna, Koushik

doi:10.1109/embc.2019.8857652

Cited by 2 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is different from electroencephalographer analysis. In the future, real-time electroencephalography studies based on machine learning technology will be needed to help develop new models and investigate this phenomenon further [51].…”

Section: Discussionmentioning

confidence: 99%

An integrative prediction algorithm of drug-refractory epilepsy based on combined clinical-EEG functional connectivity features

et al. 2021

View full text Add to dashboard Cite

Objective Although the use of antiepileptic drugs (AEDs) is routine, 30-40% of patients with epilepsy (PWEs) experience drug resistance. Thus, early identification of AED resistance will help optimize treatment regimens and improve patients' prognoses. However, there have been few studies on this topic to date. Here, we try to establish an integrative prediction model of AED resistance for drug-naive PWEs, and to identify the clinical and Electroencephalogram (EEG) factors that affect their outcomes. Methods One hundred sixty-four PWEs naive to AEDs treated at a tertiary care center from January 2014 to June 2020 were retrospectively analyzed. A total of 113 of these patients were well controlled and 53 were drug refractory with regular AED treatment for more than one year. Eighty clinical characteristics and 684 EEG functional connectivity variables based on phase lag index before drug initiation were identified. Overall, 80% of each group was chosen to establish a support vector machine (SVM) model with ten-fold cross validation, and the other 20% were used to evaluate the model's performance. Absolute weight value was used to rank the features that had impacts on classification. Results An integrative algorithm was modeled to predict AED resistance for drug-naive PWEs by SVM based on clinical characteristics and EEG functional connectivity values. The model had an accuracy of 94% [95% confidence interval (CI) 0.85-1.0], sensitivity of 95% [95% CI 0.82-1.0], specificity of 93% [95% CI 0.77-1.0], and an area under the curve (AUC) of 0.98 [95% CI 0.91-1.0]. The p values of accuracy, sensitivity specificity and AUC were calculated as 0.001, 0.001, 0.01 and 0.001, respectively. The δ band fromT4-FZ and T3-PZ, α band from T3-T6 and β band from F7-CZ and FP2-F3 were the top five EEG features that impacted the SVM classifier. ConclusionWe constructed an integrative prediction algorithm of AED resistance for drug-naive PWEs. Its utility in clinical settings should be evaluated in the future.

show abstract

Section: Discussionmentioning

confidence: 99%

An integrative prediction algorithm of drug-refractory epilepsy based on combined clinical-EEG functional connectivity features

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Exploiting the symmetric properties of PLI, the summation of the binary degree k i is equal to two times the summation of the non-zero values in the upper triangular elements of the matrix. Thus, it is possible to calculate the density as in (10) with the already calculated degree, reducing the arithmetic resources required.…”

Section: Densitymentioning

confidence: 99%

“…The architecture used for the PLI calculation is the same as the one presented in [10] and it is shown in figure 3. The main components of this architecture are the Analytical signal, Phase calculation and PLI calculation modules.…”

Section: Pli Architecturementioning

confidence: 99%

“…Finally, the PLI calculation module uses these instantaneous phases to calculate the PLI value as in (1). This architecture was extended to 19-channels in this work instead of 16-channels as originally done in [10].…”

Section: Pli Architecturementioning

confidence: 99%

“…To achieve this, in this work we propose a novel system hardware architecture for a 19-channel EEG system that has two main modules, namely, computation of phase lag index (PLI) matrix resulting into the formulation of FC and, the extraction of graph-theoretic parameters from these FCs. This architecture is based on the proof-ofconcept work presented in [10,11]. However, those two works only presented the two modules required for implementing the entire system, namely, the PLI computation and graph-theoretic parameters extraction respectively.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hardware architecture for real-time EEG-based functional brain connectivity parameter extraction

2021

View full text Add to dashboard Cite

Objective. Design a novel architecture for real-time quantitative characterization of functional brain connectivity (FC) networks derived from wearable electroencephalogram (EEG). Approach. We performed an algorithm to architecture mapping for the calculation of phase lag index to form the functional connectivity networks and the extraction of a set of graph-theoretic parameters to quantitatively characterize these networks. This mapping was optimized using approximations in the mathematical definitions of the algorithms which reduce its computational complexity and produce a more hardware amenable implementation. Main results. The architecture was developed for a 19-channel EEG system. The system can calculate all the functional connectivity parameters in a total time of 131 µs, utilizes 71% of the total logic resources in the FPGA, and shows 51.84 mW dynamic power consumption at 22.16 MHz operation frequency when implemented in a Stratix IV EP4SGX230K FPGA. Our analysis also showed that the system occupies an area equivalent to approximately 937 K 2-input NAND gates, with an estimated power consumption of 39.3 mW at 0.9 V supply using a 90 nm CMOS application specific integrated circuit technology. Significance. The proposed architecture can calculate the FC and extract the graph-theoretic parameters in real-time with low power consumption. This characteristic makes the architecture ideal for applications such as a wearable closed-loop neurofeedback systems, where constant monitoring of the brain activity and fast processing of EEG is necessary to control the appropriate feedback.

show abstract

A phase lag index hardware calculation for real-time electroencephalography studies

Cited by 2 publications

References 12 publications

An integrative prediction algorithm of drug-refractory epilepsy based on combined clinical-EEG functional connectivity features

An integrative prediction algorithm of drug-refractory epilepsy based on combined clinical-EEG functional connectivity features

Hardware architecture for real-time EEG-based functional brain connectivity parameter extraction

Contact Info

Product

Resources

About