Seediscussions,stats,andauthorprofilesforthispublicationat:https://www.researchgate.net/publication/220759367
AReconfigurableHebbianEigenfilterforNeurophysiologicalSpikeTrainAnalysis.
CONFERENCEPAPER·JANUARY2010
DOI:10.1109/FPL.2010.109·Source:DBLP
CITATIONS
3
READS
15
7AUTHORS,INCLUDING:
FeiXia
NewcastleUniversity
94PUBLICATIONS414CITATIONS
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AlexYakovlev
NewcastleUniversity
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Availablefrom:FeiXia
Retrievedon:04February2016
A Reconfigurable Hebbian Eigenfilter for Neurophysiological Spike Train Analysis
Bo Yu1,3, Terrence Mak1,2, Fei Xia1, Alex Yakovlev1, Xiangyu Li3, Yihe Sun3, Chi-Shang Poon4
1School of Electrical, Electronic & Computer Engineering
2Institute of Neuroscience Newcastle University, UK
3Institute of Microelectronic, Tsinghua University, China 4MIT-Harvard Health Science and Technology, MIT, US
2 September 2010
1 20th International Conference on Field Programmable Logic and Applications
Outline
• Background – BMI (Brain Machine Interface)
– Spike Sorting
• Hebbian Eigenfilter Based Spike Sorting Algorithm & Hardware
• Evaluation
2 20th International Conference on Field Programmable Logic and Applications
Outline
• Background – BMI (Brain Machine Interface)
– Spike Sorting
• Hebbian Eigenfilter Based Spike Sorting Algorithm & Hardware
• Evaluation
3 20th International Conference on Field Programmable Logic and Applications
Background • BMI (Brain machine interface)
– It is a direct communication pathway between brain and external devices (also called direct neural interface or brain computer interface).
4 20th International Conference on Field Programmable Logic and Applications
Background – Various BMI
5 20th International Conference on Field Programmable Logic and Applications
Background – Various BMI
6 20th International Conference on Field Programmable Logic and Applications
Background • Close-loop BMI
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Background
• Spike sorting
– Electrode may collect signal from more than one neurons.
– The need of hardware for real-time spike sorting.
8 20th International Conference on Field Programmable Logic and Applications
Outline
• Background
– BMI (Brain Machine Interface)
– Spike Sorting
• Algorithm Design & Hardware Implementation
• Evaluation
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Spike Sorting Algorithm • Our PCA based Spike Sorting Algorithm
– Algorithm Procedure
– Feature Extraction is done by PCA that is a widely used method for feature extraction.
– Classification is done by k-means clustering algorithm. It clusters data to K groups.
10 20th International Conference on Field Programmable Logic and Applications
Spike Sorting Algorithm • Our PCA based Spike Sorting Algorithm
– Algorithm Procedure
– Feature Extraction is done by PCA that is a widely used method for feature extraction.
– Classification is done by k-means clustering algorithm. It clusters data to K groups.
11 20th International Conference on Field Programmable Logic and Applications
Principal Component Analysis • Feature extraction
– Principal component analysis (PCA) algorithms require complicated numerical procedures • Eigenvalue decomposition of covariance matrix or single
value decomposition of data matrix • Covariance matrix computation, matrix inverse, matrix
diagonalization, symmetric rotation.
– We present a Hebbian eigenfilter (based on general Hebbian alogrithm (GHA) ) for PCA computation • It does not involve complex computation • It has the ability to compute only the first few most
important PCs • Easier to be implemented in hardware
12 20th International Conference on Field Programmable Logic and Applications
Spike Sorting Algorithm (I)
• Hebbian Eigenfilter
– Step 1: Initialize weight W and select learning rate
– Step 2: Pre-process to remove mean from data
– Step 3: Update weight W 1
( ( )) /N
i
x i N
( ) ( )x i x i
( ( ) )T TW y x LT y y W
W W W 13 20th International Conference on Field Programmable Logic and Applications
y W x
Spike Sorting Algorithm (II)
• K-means clustering algorithm
– K-means clustering compute K centroids in PCs’ space and cluster data into K groups
• Step 1: Initialize centroids
• Step 2: Assign each point to its nearest centroid
• Step 3: Update centroid
2
1 1
|| ||N K
nk n k
n k
J r x
nk nnk
nkn
r x
r
14 20th International Conference on Field Programmable Logic and Applications
Hardware Design • Hardware Implementation
– Structure of hardware Hebbian eigenfilter
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Hardware Design – Structure of hardware K-means algorithm
16 20th International Conference on Field Programmable Logic and Applications
Hardware Design • FPGA Implementation
– More flexible than specific hardware and higher performance than microprocessor
– Providing massive parallel computation resources,
suiting for multi-channel spike sorting hardware implementation.
Flexibility
Performance
microprocessor
Application Specific hardware
FPGA
17 20th International Conference on Field Programmable Logic and Applications
Outline
• Background
– BMI (Brain Machine Interface)
– Spike Sorting
• Algorithm & Hardware
• Evaluation
18 20th International Conference on Field Programmable Logic and Applications
Evaluation • Input spikes
– Clinical and synthetic dataset are used for the evaluation
– Synthetic spike train generating tool (University of Stirling) is used to generate synthetic data for evaluating software and hardware
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Evaluation • Benchmarks and scenarios of input spike
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Evaluation • Evaluation methodology
– Matlab and Xilinx System Generator are used for software and hardware simulations, respectively
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Evaluation • Algorithm evaluation
( )
( )
Max signalSNR
Max noise _ _
_
correct classified spike
total spike
Numcr
Num
22 20th International Conference on Field Programmable Logic and Applications
Evaluation • Hardware evaluation
, ,
, 2 , 2
| ||| || || ||
i software i hardware
i software i hardware
PC PCaccuracy
PC PC
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Evaluation • Hardware evaluation
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Evaluation • Clinical data sets
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Evaluation • Throughput and hardware resource
• Hardware is 10x faster than software • FPGA can approximately implement 20
parallel channels with real-time spike sorting
Word Length =10 bit
Software (Intel Core2 E8400@3GHz, Matlab 2009a)
Number of Logic 674(1.4%) --
Number of DSP 3(2.4%) --
Number of BRAM 45(31.8%) --
Clock Freq.(MHz) 50 --
Learning Latency (ms) 8.2 93.8
Projection Latency (ms) 43.4 10 23.2 10
26 20th International Conference on Field Programmable Logic and Applications
Conclusion
• We utilize Hebbian eigenfilter to accomplish feature extraction in PCA based spike sorting algorithm.
• We implemented Hebbian eigenfilter using FPGA and rigorously evaluated it using synthetic and clinical spike trains.
• Our future work is to improve the design and incorporate the FPGA systems into closed-loop feedback brain-machine-interface systems.
27 20th International Conference on Field Programmable Logic and Applications