Crispin-Bailey, Christopher

Dai, Chengliang

White Rose Research Online

Tradeoffs for EEG data reduction on Wearable Device

This is a repository copy of Tradeoffs for EEG data reduction on Wearable Device.White Rose Research Online URL for this paper:https://eprints.whiterose.ac.uk/98604/Conference or Workshop Item:Dai, Chengliang and Crispin-Bailey, Christopher orcid.org/0000-0003-0613-9698 (2012) Tradeoffs for EEG data reduction on Wearable Device. In: Building Bridges to Brains Workshop, 01 Nov 2012. eprints@whiterose.ac.ukhttps://eprints.whiterose.ac.uk/Reuse Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Chengliang  Dai,    Dr  Christopher  Crispin-Bailey Department  of  Computer  Science,  University  of  York Bibliograph [1] E.  Waterhouse,  "New  horizons  in  ambulatory  electroencephalography,"  IEEE  Eng.  Med.  Biol.  Mag.,  vol.  22,  no.  3,  pp.  74–80,  2003.   [2] G.  Antoniol  and  P.  Tonella,  "EEG  data  compression  techniques,"  IEEE  Trans.  Biomed.  Eng.,  vol.  44,  pp.  105–114,  1997.     [3] S.  Mallat,  A  Wavelet  Tour  of  Signal  Processing,  2nd  ed.  San  Diego,  CA:  Academic,  1999.   [4] R.  G.  Andrzejak,  K.  Lehnertz,  F.  Mormann,  C.  Rieke,,  P.  David,  &  C.  E.  Elger,  "Indications  of  nonlinear  deterministic  and  finite  di              mensional  structures  in  time  series  of  brain  electrical  activity:  Dependence  on  recording  region  and  brain  state,"  Phys.  Rev.  E,  vol.  64,  no.  6,  pp.      (061907)1–8,  2001.     [5] Y.  Wongsawat,  S.  Oraintara,  T.  Tanaka,  and  K.  R.  Rao,  "Lossless  multi-channel  EEG  compression,"  in  Proceedings  of  IEEE  Int.  Symp.  on  Circ.  and  Syst.  (ISCAS  ’06),  pp.  1611–1614,  Island  of  Kos,  Greece,  2006.     [6] A.  N.  Akansu  and  M.  J.  T.  Smith,  Subband  and  Wavelet  Transforms:  Design  and  Applications,  Kluwer  Academic  Publishers,  1995.       [7]  h p://www.nordicsemi.com/eng/Products/2.4GHz-RF/nRF24L01 Introduction Electroencephalogram	  (EEG)	  has	  been	  proved	  as	  a	  noninvasive	  and	  reliable	  way	  for	  collecting	  a	  per-­‐son’s	  brain	  wave	  data	  which	  can	  be	  used	  in	  numerous	  areas	  such	  as	  epilepsy	  diagnosis	  [1],	  brain-computer	  interface	  (BCI),	  etc.	  	     Wearable	  EEG	  recorder	  has	  drawn	  a	  lot	  of	  attention	  in	  recent	  years	  since  it  saves  much  more   me  and  money    for  researchers  com-­‐paring  to  tradi onal  data  collec ng  equipment.    A  wearable  EEG  recorder  can  be  as  small  as  a  10p  coin  (Figure  1)  so  that  the  ob-­‐ject  of  recording  will  not  even  no ce  it.  Such  device  is  preferable  to  be  able  to  transmit  the  data  via  wireless  transceiver  to  achieve  the  real– me  monitoring  and  data  analysis.  However,    wireless  transmission  apparently  shortens  device’s  running   me  because  of  the  extra  power  consumed  by  the  transceiver.    The  most  obvious  way  to  extend  running   me  is  to  compress  the  EEG  data  before  transmission,  and  with  less  data  to  be  trans-­‐mi ed,  the  overall  power  consump on  will  be  largely  reduced  (Figure  3).   Meanwhile,  adding  on  a  data  compressor  to  the  device  also  in-­‐creases  the  complexity  of  the  system,  and  compression  unit  it-­‐self  is  one  of  the  contributors  to  recorder’s  power  consump on,  so  exploring  a  best  set  of  trade-offs  between  compression  ra o  (CR)  and  the  power  consump on  of  compressing  the  data  will  be  helpful  to  solve  all  these  problems,  and  that  is  the  purpose  of  doing  this  research. Figure	  1.	  A	  wearable	  EEG	  recorder	  with	  a	  4-channel	  input	  	  designed	  by	  Advanced	  Computer	  Architecture	  Group	  in	  Univer-­‐sity	  of	  York	   Figure	  2.	  Current	  solution	  of	  reading	  in	  the	  EEG	  data,	  a	  docking	  station. Figure	  3.	  A	  simpli ied	  architecture	  of	  wearable	  EEG	  recorder Experiments Several  compression  method  candidates  have  been  simulated  so  far  including  most  well–known  lossless  Huffman  coding  [2],  a  lossy  encoding  technique  called  Discrete  Wavelet  Transform  (DWT)  [3],  as  well  as  a  lossless  log2  sub-band  algorithm  we  designed  which  will  be  introduced  in  detail  below.     Currently,  EEG  signals  are  usually  quan zed  to  no  more  than  16  bits  in  most  experiments,  and  take  this  resolu on  as  an  example,    in  log2  sub-band  algorithm  scheme,  we  first  compare  the  first  4  bits  from  16  bits  of  the  current  sample  with  the  same  part  in  previous  sample,  and  if  they  are  iden cal,  next  4  bits  will  be  further  compared,  and  the  transceiver  only  transmits  the  bits  that  are  different  from  previous  sample.  A  flag  of  2  bits  is  introduced  to  indicate  how  many  nibbles  are  transmi ed  in  a  sample  (Figure  4),  which  can  be  used  to  decompress  the  data. The  dataset  used  for  simula on  come  from  an  EEG  experiment  taken  in  University  Hospital  of  Bonn  [4],  and  the  sampling  rate  of  data  is    173.61  Hz,  and  are  quan zed  into  10  bits  for  the  simula on. Figure	  4.	  ‘00’	  indicates	  that	  4	  nibbles	  of	  this	  sample	  are	  transmit-­‐ted,	  	  ‘01’	  indicates	  3,	  ‘10’	  indicates	  2,	  and	  ‘11’	  indicates	  1	  nibble. Contact	  :	   Chengliang	  Dai cd633@york.ac.uk Advanced	  Computer	  Architecture	  Group,	  	   Department	  of	  Computer	  Science,	   University	  of	  York,	  Heslington,	  York,	  YO10	  5GH,	  UK	   Conclusions For  a  simplified  wearable  EEG  recorder,    which  is  only  consisted  with  amplifiers,  ADCs,  and  the  transceiv-­‐er,  and  power  consump on  of  DSPs  and  the  processor  are  neglected,  the  overall  power  consump on  of  the  recorder  can  be  given  as   Where     and     are  the  power  consump on  of  amplifiers  and  ADCs,  and     and     are  the  power  consump on  of  transceiver  and  doing  data  compression.  Then  the  power  reduc on  is  By  implemen ng  a  popular  off-the-shelf  GFSK  transceiver  nRF24L01  [7](TX  power  consump on  is  23  mW),  the  power  saving  for  using  Huffman  coding  is  at  least  5.5mW,  11.5mW  for  using  DWT,  and  3.45mW  for  using  log2  sub-band  algorithm.  As  a  result,  these  are  power  budgets  for  future  hardware  implementa on  of  data  compression  techniques,  and  any  technique  requires  lower  power  than  its  saving  is  considered  as  a  proper  method  which  leads  to  a  longer  working   me  of  wearable  EEG  recorder.  Furthermore,    possible  data  compression  techniques  may  also  applicable  to  other  biomedical  data  recorders  as  these  signals  have  many  similari es. 𝑃𝑠𝑦𝑠 = 𝑃𝐴𝑚𝑝 + 𝑃𝐴𝐷𝐶 + 𝑃𝐶𝑜𝑚𝑝 + 𝐶𝑅 ∗ 𝑃𝑇𝑥  Results The  CR  is  defined  as                                                                                                         CR= The  data  set  for  tes ng  the  Huffman  coding  is  divided  into  2  subsets,  and  each  contains  10  files  with  4096  data  samples  in  every  file.  One  subset  is  used  to  generate  the  Huffman  tree  (dic onary)  so  that  the  other  subset  can  be  encoded  based  on  the  previous  dic onary.  The  result  of  log2  sub-band  algorithm  will  also  shown  in  the  Figure  5. According  to  [5],    the  DC-bias  of  EEG  signal  degrades  the  inter-channel  decorrela on  performance,  and  it   can  be  simply  removed  by  using  the  differen al  pulse-code  modula on  (DPCM)  which  calculates  the  difference    between  adjacent  samples.  The  performance  of  Huffman  coding  and  log2  sub-band  algorithm  are  both  increased  a er  applying  DPCM  (Figure  5).   As  for  DWT,  it  expresses  a  signal  as  a  weighted  sum  of  basis  func ons,  and  these  basis  are  de-­‐rived  from  dilated  and  translated  versions  of  a  func on  which  is  called  mother  wavelet  [6].  So  the  original  signal  can  be  defined  with  coefficients  of  the  set  of  basis  func ons,  and  this  process  causes  some  loss  of  the  original  signals,  but  as  long  as  a  op mal  mother  wavelet  is  chosen,  the  discrepan-­‐cies  between  original  and  reconstructed  signals  are  s ll  acceptable.  One  file  with  4096  data  sam-­‐ples  is  used  for  simula on,  and  the  mother  wave-­‐let  applied  are    Haar  (Figure  6),  Daubechies  10  (Figure  7),  and  Coiflet  (Figure  8)  [3].    The  recon-­‐structed  signal  is  recovered  with  50  percent  coeffi-­‐cients  of  original  signal,  so  the  CR  is  0.5  in  this  case,  and  as  they  are  shown  in  the  figures,  using  Haar  as  mother  wavelet  gives  worst  reconstruc-­‐on  quality,  and  the  results  of  using  Daubechies  10  and  Coiflet  are  close. Figure	  5.	  Compression	  Ratio	  of	  presented	  techniques	   Figure	  6.	  Reconstructed	  with	  Harr,	  and	  from	  top	  to	  bottom	  are	  original	  signal,	  reconstructed	  signal	  and	  differentials Figure	  7.	  Reconstructed	  with	  Daubechies	  10 Figure	  8.	  Reconstructed	  with	  Coi let 

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Tradeoffs for EEG data reduction on Wearable Device

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