Architecture for complex network measures of brain connectivity

Pal, Chandrajit; Biswas, Dwaipayan; Maharatna, Koushik; Chakrabarti, Amlan

doi:10.1109/iscas.2017.8050239

Cited by 7 publications

(12 citation statements)

References 9 publications

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“…To perform the CPL calculation it is necessary to map the weights of every edge in the matrix to an equivalent distance. Here, we decided to perform the mapping as in (11). With this mapping, the weight values closer to one which represents a strong connection on the brain connectivity matrix will have a small distance value.…”

Section: Characteristic Path Length (Cpl)mentioning

confidence: 99%

“…Once the mapping is done, the shortest path is found using DA discussed in section 2. The shortest path finding unit in figure 7(a), performs the distance mapping shown in (11) and compares the actual minimum distance from the node against the particular node distance plus the current node distance, as it is done in step 4 in section 2. The Minimum finding unit finds the node with the minimum distance that will be used as the new node (step 5) and the current node distance is updated with this node distance.…”

Section: Characteristic Path Length (Cpl)mentioning

confidence: 99%

“…In literature, only the authors from [11] have presented an architecture that performs a calculation of the graph connectivity measurements. The comparison between this and the proposed system is presented in table 8.…”

Section: Comparisonmentioning

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%

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

2021

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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.

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Section: Characteristic Path Length (Cpl)mentioning

confidence: 99%

Section: Characteristic Path Length (Cpl)mentioning

confidence: 99%

Section: Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2021

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“…The research of identifying influential nodes in complex networks has attracted great interests in recent years because of its great significance on many applications, such as: (1) In economic field, researchers use relevant methods to analyze important countries and regions in the global economic system [1]; (2) In power engineering field, researchers use relevant methods to analyze influential nodes and critical lines in the power transmission network [2]; (3) In advertising field, researchers use relevant methods to find the optimal advertising strategy [3]; (4) In big data analysis field, researchers use relevant methods to find key information in massive data [4]; (5) In life science field, researchers use relevant methods to analyze key neurons in brain neural networks and key proteins in protein interaction networks [5]; (6) In network immunity field, the key target is to find key nodes for…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Based Framework for Identifying Influential Nodes in Complex Networks

et al. 2020

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In complex networks, identifying influential nodes is of great importance for its wide applications. Traditional centrality methods are usually directly based on topological structures of networks, and different centrality methods consider different structural characteristics related to the functional importance. However, in many scenarios, it always exists a complex and nonlinear relationship between the functional importance of a node and its various features including local location, global location, etc., which is hard to be described by one centrality. In order to solve this problem, this paper proposes a framework based on machine learning to measure the importance of nodes in the propagation scenario. This framework first constructs the feature vector of each node based on the existing centrality methods which can reflect nodes' different topological structures and the infection rate which is an important factor in the propagation scenarios, then labels each node based on the real propagation ability obtained from simulated propagation experiments based on SIR model, last uses seven machine learning algorithms to learn the complex relationship between the real propagation ability of each node and its various structural features. The experimental results in real-world networks show that the classification accuracy of the model based on machine learning is generally higher than that of the traditional centrality methods based on one certain topology. INDEX TERMS Complex networks, influential nodes, machine learning, centrality.

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A Novel Hybrid Model for Brain Functional Connectivity Based on EEG

Zhang

et al. 2021

Lecture Notes in Computer Science

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Architecture for complex network measures of brain connectivity

Cited by 7 publications

References 9 publications

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

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

A Machine Learning Based Framework for Identifying Influential Nodes in Complex Networks

A Novel Hybrid Model for Brain Functional Connectivity Based on EEG

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