Abstract
Motivated by current attempts to use wireless in Brain-Machine Interfaces (BMIs), this paper presents a method for the compression of spike data. Supported by Vector Quantization (VQ) theory, we use a 1-dimensional Self-Organizing Map (SOM) to quantize vectors of input samples. The indices are entropy coded to further reduce the necessary bandwidth, taking advantage of the non-uniform frequency of firing of the SOM processing elements (PEs). The complexity of the use of the SOM is also considered and addressed. After training several SOMs, the method was simulated with real data achieving compression ratios as high as 185.7:1, i.e. a bitrate of 862 bits-per-second-per-channel, assuming sampling at 20 kHz with 8 bits-per-sample (bps).
| Original language | English |
|---|---|
| Title of host publication | 2nd International IEEE EMBS Conference on Neural Engineering |
| Pages | 233-236 |
| Number of pages | 4 |
| Volume | 2005 |
| DOIs | |
| State | Published - Dec 1 2005 |
| Externally published | Yes |
| Event | 2nd International IEEE EMBS Conference on Neural Engineering, 2005 - Arlington, VA, United States Duration: Mar 16 2005 → Mar 19 2005 |
Other
| Other | 2nd International IEEE EMBS Conference on Neural Engineering, 2005 |
|---|---|
| Country | United States |
| City | Arlington, VA |
| Period | 3/16/05 → 3/19/05 |
Fingerprint
ASJC Scopus subject areas
- Engineering(all)
Cite this
Compression of spike data using the self-organizing map. / Paiva, António R C; Príncipe, José C.; Sanchez, Justin C.
2nd International IEEE EMBS Conference on Neural Engineering. Vol. 2005 2005. p. 233-236 1419599.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Compression of spike data using the self-organizing map
AU - Paiva, António R C
AU - Príncipe, José C.
AU - Sanchez, Justin C.
PY - 2005/12/1
Y1 - 2005/12/1
N2 - Motivated by current attempts to use wireless in Brain-Machine Interfaces (BMIs), this paper presents a method for the compression of spike data. Supported by Vector Quantization (VQ) theory, we use a 1-dimensional Self-Organizing Map (SOM) to quantize vectors of input samples. The indices are entropy coded to further reduce the necessary bandwidth, taking advantage of the non-uniform frequency of firing of the SOM processing elements (PEs). The complexity of the use of the SOM is also considered and addressed. After training several SOMs, the method was simulated with real data achieving compression ratios as high as 185.7:1, i.e. a bitrate of 862 bits-per-second-per-channel, assuming sampling at 20 kHz with 8 bits-per-sample (bps).
AB - Motivated by current attempts to use wireless in Brain-Machine Interfaces (BMIs), this paper presents a method for the compression of spike data. Supported by Vector Quantization (VQ) theory, we use a 1-dimensional Self-Organizing Map (SOM) to quantize vectors of input samples. The indices are entropy coded to further reduce the necessary bandwidth, taking advantage of the non-uniform frequency of firing of the SOM processing elements (PEs). The complexity of the use of the SOM is also considered and addressed. After training several SOMs, the method was simulated with real data achieving compression ratios as high as 185.7:1, i.e. a bitrate of 862 bits-per-second-per-channel, assuming sampling at 20 kHz with 8 bits-per-sample (bps).
UR - http://www.scopus.com/inward/record.url?scp=33744504181&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33744504181&partnerID=8YFLogxK
U2 - 10.1109/CNE.2005.1419599
DO - 10.1109/CNE.2005.1419599
M3 - Conference contribution
AN - SCOPUS:33744504181
VL - 2005
SP - 233
EP - 236
BT - 2nd International IEEE EMBS Conference on Neural Engineering
ER -