Hosaka/WhiteICE wiki - User contributions [en]

Main Page

2026-07-11T08:48:23Z

Cslr:

Hosaka/WhiteICE information

Idea of the software is that you run the sound stimulation in background while using a computer. Stimulation requires many hours of measurements before it is effective. Sounds used are very simple because the learning starts from scratch (TODO: collect a database of measurements and models that can be used without a long training time + generate music instead of plain sounds).

Command-line version of WhiteICE has more options. You start listening a LSL stream from EEG in a local network by using a command:

<code>
whiteice --measure --device=lsl --concentration --lsl-channels=4
</code>

The command plays sounds in the background (while using a computer), tries to increase concentration and uses first 4 channels found from the first LSL stream found from the network. If you are using "BrainAccess MINI EEG" for example which has 8 channels, you use command "--lsl-channels=8". The default number of LSL channels is 4.

In practice you need to run software for hours to get small average positive effect to target values.

If you have Interaxon Muse instead, you must configure MindMonitor app to send OSC stream to your computers IP and port 4545 and use a command (the number of channels is always 4):

<code>
whiteice --measure --device=mindmonitor --concentration
</code>

Also read: [[WhiteICE version information]]

Main Page

2026-07-11T08:47:37Z

Cslr:

Hosaka/WhiteICE information

Idea of the software is that you run the sound stimulation in background while using a computer. Stimulation requires many hours of measurements before it is effective. Sounds used are very simple because the learning starts from scratch (TODO: collect database of measurements and models that can be used without long training + generate music instead of plain sounds).

Command-line version of WhiteICE has more options. You start listening a LSL stream from EEG in a local network by using a command:

<code>
whiteice --measure --device=lsl --concentration --lsl-channels=4
</code>

The command plays sounds in the background (while using a computer), tries to increase concentration and uses first 4 channels found from the first LSL stream found from the network. If you are using "BrainAccess MINI EEG" for example which has 8 channels, you use command "--lsl-channels=8". The default number of LSL channels is 4.

In practice you need to run software for hours to get small average positive effect to target values.

If you have Interaxon Muse instead, you must configure MindMonitor app to send OSC stream to your computers IP and port 4545 and use a command (the number of channels is always 4):

<code>
whiteice --measure --device=mindmonitor --concentration
</code>

Also read: [[WhiteICE version information]]

Main Page

2026-07-11T08:29:41Z

Cslr:

Hosaka/WhiteICE information

Idea of the software is that you run the sound stimulation in background while using a computer. Stimulation requires many hours of measurements before it is effective.

Command-line version of WhiteICE has more options. You start listening a LSL stream from EEG in a local network by using a command:

<code>
whiteice --measure --device=lsl --concentration --lsl-channels=4
</code>

The command plays sounds in the background (while using a computer), tries to increase concentration and uses first 4 channels found from the first LSL stream found from the network. If you are using "BrainAccess MINI EEG" for example which has 8 channels, you use command "--lsl-channels=8". The default number of LSL channels is 4.

In practice you need to run software for hours to get small average positive effect to target values.

If you have Interaxon Muse instead, you must configure MindMonitor app to send OSC stream to your computers IP and port 4545 and use a command (the number of channels is always 4):

<code>
whiteice --measure --device=mindmonitor --concentration
</code>

Also read: [[WhiteICE version information]]

WhiteICE version information

2026-07-11T00:14:29Z

Cslr:

WhiteICE v1.39b (in development)

New version with improved RL algorithm that SHOULD give more often positive results (in many hours long stimulation). Stimulation interval is 1000ms. Effect seem to be only about 1% with this new code.

Program learns very slowly, 3h 20minutes (300 models) didn’t show big improvements. Try to increase tau from 0.02 to 0.1 and see if it still increases performance a bit. => tau increase seem to give worse results???

Added forgetting to reinforcemen learning, now 30 minutes older measurements are not used. TODO: increase tau from 0.02 to 0.1 (for testing) so model can learn temporal dynamics better.

DONE: Added additional measurements to RL's state vector.

NOW: 5000 msecs stimulation time seems to give much better results than 1000 ms (2-3% effect). However, forgetting time should be maybe increased from 30minutes to 60minutes? (TODO: test it) AND test 10 sec long stimulus again to see if there is better effect.

WhiteICE v1.38b

This version re-enabled longer stimulation interval 5000ms which seem to have positive effect to "episodes-result.txt" average reinforcement value time-series when stimulatio is run for many hours. Typical effect seem to be 5% (versus random stimulation)

WhiteICE v1.37b

This experimental version used very short stimulation interval 500ms.

WhiteICE version information

2026-07-10T23:46:00Z

Cslr:

WhiteICE v1.39b (in development)

New version with improved RL algorithm that SHOULD give more often positive results (in many hours long stimulation). Stimulation interval is 1000ms. Effect seem to be only about 1% with this new code.

Program learns very slowly, 3h 20minutes (300 models) didn’t show big improvements. Try to increase tau from 0.02 to 0.1 and see if it still increases performance a bit. => tau increase seem to give worse results???

Added forgetting to reinforcemen learning, now 30 minutes older measurements are not used. TODO: increase tau from 0.02 to 0.1 (for testing) so model can learn temporal dynamics better.

DONE: Added additional measurements to RL's state vector.

NOW: 5000 msecs stimulation time seems to give much better results than 1000 ms (2-3% effect). However, forgetting time should be maybe increased from 30minutes to 60minutes? (TODO: test it)

WhiteICE v1.38b

This version re-enabled longer stimulation interval 5000ms which seem to have positive effect to "episodes-result.txt" average reinforcement value time-series when stimulatio is run for many hours. Typical effect seem to be 5% (versus random stimulation)

WhiteICE v1.37b

This experimental version used very short stimulation interval 500ms.

WhiteICE version information

2026-07-10T23:15:28Z

Cslr:

WhiteICE v1.39b (in development)

New version with improved RL algorithm that SHOULD give more often positive results (in many hours long stimulation). Stimulation interval is 1000ms. Effect seem to be only about 1% with this new code.

Program learns very slowly, 3h 20minutes (300 models) didn’t show big improvements. Try to increase tau from 0.02 to 0.1 and see if it still increases performance a bit. => tau increase seem to give worse results???

Added forgetting to reinforcemen learning, now 30 minutes older measurements are not used. TODO: increase tau from 0.02 to 0.1 (for testing) so model can learn temporal dynamics better.

DONE: Added additional measurements to RL's state vector.

NOW: 5000 msecs stimulation time seems to give much better results than 1000 ms. However, forgetting time should be maybe increased from 30minutes to 60minutes? (TODO: test it)

WhiteICE v1.38b

This version re-enabled longer stimulation interval 5000ms which seem to have positive effect to "episodes-result.txt" average reinforcement value time-series when stimulatio is run for many hours. Typical effect seem to be 5% (versus random stimulation)

WhiteICE v1.37b

This experimental version used very short stimulation interval 500ms.

WhiteICE version information

2026-07-07T18:54:31Z

Cslr:

WhiteICE v1.39b (in development)

New version with improved RL algorithm that SHOULD give more often positive results (in many hours long stimulation). Stimulation interval is 1000ms. Effect seem to be only about 1% with this new code.

Program learns very slowly, 3h 20minutes (300 models) didn’t show big improvements. Try to increase tau from 0.02 to 0.1 and see if it still increases performance a bit. => tau increase seem to give worse results???

Added forgetting to reinforcemen learning, now 30 minutes older measurements are not used. TODO: increase tau from 0.02 to 0.1 (for testing) so model can learn temporal dynamics better.

WhiteICE v1.38b

This version re-enabled longer stimulation interval 5000ms which seem to have positive effect to "episodes-result.txt" average reinforcement value time-series when stimulatio is run for many hours. Typical effect seem to be 5% (versus random stimulation)

WhiteICE v1.37b

This experimental version used very short stimulation interval 500ms.

WhiteICE version information

2026-07-07T18:47:30Z

Cslr:

WhiteICE v1.39b (in development)

New version with improved RL algorithm that SHOULD give more often positive results (in many hours long stimulation). Stimulation interval is 1000ms. Effect seem to be only about 1% with this new code.

Program learns very slowly, 3h 20minutes (300 models) didn’t show big improvements. Try to increase tau from 0.02 to 0.1 and see if it still increases performance a bit. => tau increase seem to give worse results???

Added forgetting to reinforcemen learning, now 30 minutes older measurements are not used. TODO: increase tau to 0.1 (for testing) so model can learn temporal dynamics better.

WhiteICE v1.38b

This version re-enabled longer stimulation interval 5000ms which seem to have positive effect to "episodes-result.txt" average reinforcement value time-series when stimulatio is run for many hours. Typical effect seem to be 5% (versus random stimulation)

WhiteICE v1.37b

This experimental version used very short stimulation interval 500ms.

Main Page

2026-07-06T22:08:13Z

Cslr:

Hosaka/WhiteICE information

Command-line version of WhiteICE has more options. You start listening a LSL stream from EEG in a local network by using a command:

<code>
whiteice --measure --device=lsl --concentration --lsl-channels=4
</code>

The command plays sounds in the background (while using a computer), tries to increase concentration and uses first 4 channels found from the first LSL stream found from the network. If you are using "BrainAccess MINI EEG" for example which has 8 channels, you use command "--lsl-channels=8". The default number of LSL channels is 4.

In practice you need to run software for hours to get small average positive effect to target values.

If you have Interaxon Muse instead, you must configure MindMonitor app to send OSC stream to your computers IP and port 4545 and use a command (the number of channels is always 4):

<code>
whiteice --measure --device=mindmonitor --concentration
</code>

Also read: [[WhiteICE version information]]

WhiteICE version information

2026-07-06T21:15:06Z

Cslr:

Main Page

2026-07-06T21:13:30Z

Cslr:

Hosaka/WhiteICE information

Command-line version of WhiteICE has more options. You start listening a LSL stream from EEG in a local network by using a command:

<code>
whiteice --measure --device=lsl --concentration --lsl-channels=4
</code>

The command plays sounds in the background (while using a computer), tries to increase concentration and uses first 4 channels found from the first LSL stream found from the network. If you are using "BrainAccess MINI EEG" for example which has 8 channels, you use command "--lsl-channels=8". The default number of LSL channels is 4.

In practice you need to run software for hours to get small average positive effect to target values.

If you have Interaxon Muse instead, you must configure MindMonitor app to send OSC stream to your computers IP and port 4545 and use a command (number of channels is always 4):

<code>
whiteice --measure --device=mindmonitor --concentration
</code>

Also read: [[WhiteICE version information]]

WhiteICE version information

2026-07-05T15:00:32Z

Cslr:

WhiteICE v1.39b

New version with improved RL algorithm that give more often positive results (in many hours long stimulation). Stimulation interval is 5000ms. Effect seem to vary between 1-5% (average distance change)

Program learns very slowly, 3h 20minutes (300 models) didn’t show big improvements. Try to increase tau from 0.02 to 0.1 and see if it still increases performance a bit.

WhiteICE v1.38b

This version re-enabled longer stimulation interval 5000ms which seem to have positive effect to "episodes-result.txt" average reinforcement value time-series when stimulatio is run for many hours. Typical effect seem to be 5% (versus random stimulation)

WhiteICE v1.37b

This experimental version used very short stimulation interval 500ms.

Main Page

2026-07-05T14:15:31Z

Cslr:

Hosaka/WhiteICE information

Command-line version of WhiteICE has more options. You start listening LSL stream from EEG in a local network by using a command:

<code>
whiteice --measure --device=lsl --concentration --lsl-channels=4
</code>

The command plays sounds in the background (while using a computer), tries to increase concentration and uses first 4 channels found from the first LSL stream found from the network. If you are using "BrainAccess MINI EEG" for example which has 8 channels, you use command "--lsl-channels=8". The default number of LSL channels is 4.

In practice you need to run software for hours to get small average positive effect to target values.

If you have Interaxon Muse instead, you must configure MindMonitor app to send OSC stream to your computers IP and port 4545 and use a command (number of channels is always 4):

<code>
whiteice --measure --device=mindmonitor --concentration
</code>

Also read: [[WhiteICE version information]]

WhiteICE version information

2026-07-05T14:14:49Z

Cslr:

WhiteICE v1.39b

New version with improved RL algorithm that give more often positive results (in many hours long stimulation). Stimulation interval is 5000ms. Effect seem to vary between 1-5% (average distance change)

WhiteICE v1.38b

This version re-enabled longer stimulation interval 5000ms which seem to have positive effect to "episodes-result.txt" average reinforcement value time-series when stimulatio is run for many hours. Typical effect seem to be 5% (versus random stimulation)

WhiteICE v1.37b

This experimental version used very short stimulation interval 500ms.

WhiteICE version information

2026-07-05T14:14:03Z

Cslr: Created page with "WhiteICE v1.39b New version with improved RL algorithm that give more often positive results (in many hours long stimulation). Stimulation interval is 5000ms. WhiteICE v1.38b This version re-enabled longer stimulation interval 5000ms which seem to have positive effect to "episodes-result.txt" average reinforcement value time-series when stimulatio is run for many hours. Typical effect seem to be 5% ??? WhiteICE v1.37b</stron..."

WhiteICE v1.39b

New version with improved RL algorithm that give more often positive results (in many hours long stimulation). Stimulation interval is 5000ms.

WhiteICE v1.38b

This version re-enabled longer stimulation interval 5000ms which seem to have positive effect to "episodes-result.txt" average reinforcement value time-series when stimulatio is run for many hours. Typical effect seem to be 5% ???

WhiteICE v1.37b

This experimental version used very short stimulation interval 500ms.

Main Page

2026-07-05T14:09:36Z

Cslr:

Main Page

2026-07-05T13:54:33Z

Cslr: WhiteICE documentation