IntracorticalSpellingALS/KIAP_BCI_speller/2019-07-04/config_dump_13_09_05.yaml

system:
  plot: 0

general:
  debug: 1
  clear_trial_history: False

daq:
  n_channels_max: 128
  # n_channels: 2
  # exclude_channels: []  # These are BlackRock channel IDs (1-based).
  exclude_channels: []
  # car_channels: [] # channel IDs to use for common average reference. This is useful
  #   for spike band power and LFP calculations
  car_channels: []
  fs: 30000.          # sampling frequency
  smpl_fct: 30        # downsample factor
  trigger_len: 50     # length of triggers <--- review: this parameter only appears in a commented out line
  daq_sleep: 0.1      # s           <--- review: this parameter does not seem to be used anywhere
  
  normalization:
    len:  600.0           # in seconds. Length of normalization period
    do_update:  false        # Performs automatic updates if true
    update_interval: 10.0   # in seconds. Defines in what intervals the rate normalization will be updated
    range: [10, 90]    # centiles, for automated normalization

    clamp_firing_rates: True
    # if use_all_channels is False:
    # channel firing rate r will be clamped and normalized (r_n):
    # r_n = (max(bottom, min(top, r)) - bottom) / (top - bottom)
    # if the channel is set to 'invert', then r_n := 1 - r_n
    # All normalized rates are then averaged.
    # otherwise, all channels will be averaged first, then normalized
    
    use_all_channels: false      # if True, all channels will be used. If False, channels as specified below will be used
    all_channels: {bottom: 1.15625, top: 1.59375, invert: false}
    
    channels:
      - {id: 24, bottom: 3.0, top: 7.0, invert: true}
      - {id: 99, bottom: 5.0, top: 12.0, invert: false}

  spike_rates:
    n_units: 1        # number of units per channel
    bin_width: 0.05    # sec, for spike rate estimation
    loop_interval: 50  # ms 
    method: 'boxcar'   # exponential or boxcar
    decay_factor: .9    # for exponential decay, for each step back, a bin's count will be multiplied by decay_factor
    max_bins: 20      # for exponential and boxcar methods, determines numbers of bins in history to take into account
    # bl_offset: 0.000001      # baseline correction constant
    bl_offset: 30.      # baseline correction constant
    # bl_offset: 0.1        # baseline correction constant
    correct_bl: False       # for online mode
    correct_bl_model: False # for offline mode


buffer:
  length: 600         # buffer shape: (length, channels)

session:  
  flags:
    bl: True
    bl_rand: True 
    decode: True
    stimulus: True


recording:
  timing:
    t_baseline_1: 5.                   # sec, trial-1 baseline duration
    t_baseline_all: 1.                 # sec, all other trials
    t_baseline_rand: 1.                # sec, add random inter-trial interval between 0 and t_baseline_rand IF session.flags.bl_rand is True
    t_after_stimulus: 0.0        
    t_response: 5.                     # sec, trial response duration
    decoder_refresh_interval: .01     # sec, for continuous decoding, the cycle time of the decoder
    bci_loop_interval: .05             # sec, step for bci thread loop
    recording_loop_interval: .05       # sec, step for bci thread loop
    recording_loop_interval_data: .02       # sec, step for data process loop

classifier:
    max_active_ch_nr: []
    # include_channels: [38, 43, 50, 52, 56, 61, 65, 67, 73, 81, 87, 88, 91]
    # include_channels: [0,   1,   4,   7,   8,  12,  14,  19,  22,  26,  28,  29,  31, 96, 100, 103]
    # include_channels: range(0,128)
    include_channels: [20]
    # include_channels: [0,   1,   4,   6, 7,   8,  12,  14,  19, 20, 22,  26,  28,  29,  31, 96, 100, 103, 121]
    # include_channels: [  1,   2,   3,   9,  19,  29,  41,  44,  48,  51,  52,  53,  54,
         # 62,  63,  66,  74,  82,  94,  95, 113]
    # exclude_channels: []
    exclude_channels: [  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,
        13,  14,  15,  16,  17,  18,  19,  21,  22,  23,  24,  25,  26,  27,
        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,
        41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,
        54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,
        67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,  79,
        80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,  92,
        93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104, 105,
       106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118,
       119, 120, 121, 122, 123, 124, 125, 126, 127]
       
    # exclude_channels: range(32,96)
    # exclude_channels: []
    n_triggers: 2     # DO NOT CHANGE THIS
    n_classes: 2
    template: [10, 14, 18, 22, 26, 30, 34, 38]
    trigger_pos: 'start'   # 'start' or 'stop' 

    online: False        # will be overwritten by code, see bci.py
    thr_prob: 0.8
    thr_window: 40 #30    # number of samples for prob above threshold to trigger decision
    break_loop: True      # True: move on as soon as decision is there, otherwise wait t_response time

    # models to use for online decoding
    path_model1: '/data/clinical/neural/fr/2019-06-26/model1_104948.pkl'   # scikit
    path_model2: '/data/clinical/neural/fr/2019-06-26/model2_104948.pkl'   # explicit LDA
    exclude_data_channels: []
    n_neg_train: 100000
    deadtime: 40



    model_training:
      save_model: False
      model: 'scikit'  # eigen, scikit, explicit
      solver: 'lsqr'    # svd, lsqr, eigen
      cross_validation: True
      n_splits:  5     # for cross-validation
      test_size: .2     # float between (0,1) or int (absolute number of trials, >=n_classes)
      reg_fact: 0.3       # regularization factor
      fsel: False          # feature selection
      triggers_plot: 3

    peaks:          # these values are for offline training
      # height: 0.9   # probability threshold
      # width: 28      # min number of samples above threshold
      distance: 40  # number of samples for peaks to be apart
      sig: 'pred'   # 'prob', 'pred' -> signal based on probabilities or prediction class
      prefilter: False

    psth:
      cut : [-40, 100]

lfp:
  fs: 1000               # sampling rate
  sampling_ratio: 30
  filter_fc_lb: [10, 0]    # cut-off frequencies for filter
  filter_fc_mb: [12, 40]    # cut-off frequencies for filter
  filter_fc_hb: [60, 250]    # cut-off frequencies for filter
  filter_order_lb: 2     
  filter_order_mb: 6     
  filter_order_hb: 10     
  artifact_thr: 400      # exclude data above this threshold
  array1: range(32,64) #3 4 7 8 10 14 17 15 44
  array21: range(2)
  # array22: range(100,112)
  array22: [] #range(96,128)
  array1_exclude: []
  array2_exclude: []
  i_start: 0 #None            # import data from start index
  i_stop:  -1 #600000 #None             # to stop index
  psth_win: [-1000, 5000]
  exclude: False
  normalize: False
  zscore: False
  car: True
  sub_band: 1
  motor_mapping: ['Zunge', 'Schliesse_Hand', 'Oeffne_Hand', 'Bewege_Augen', 'Bewege_Kopf']

  spectra:
    spgr_len: 500
  plot:
    ch_ids: [0]     # relative id of imported channel
    general: True
    filters: False

    
cerebus:
    instance: 0
    buffer_reset: True
    buffer_size_cont: 30001
    buffer_size_comments: 500

file_handling:
  data_path: '/data/clinical/neural/fr/'
  # data_path: '/home/vlachos/devel/vg/kiapvmdev/data/clinical/neural_new/'

  results: '/data/clinical/nf/results/'
  paradigm_config_file: 'paradigm.yaml'
  # results: '/home/vlachos/devel/vg/kiapvmdev/data/clinical/neural_new/results/'
  # data_path: '/media/vlachos/kiap_backup/Recordings/K01/laptop/clinical/neural/'
  # data_path: '/media/kiap/kiap_backup/Recordings/K01/Recordings/20190326-160445/'
  save_data: True     # keep always True
  mode: 'ab'   # ab: append binary, wb: write binary (will overwrite existing files)
  git_hash: 7bd679a
  filename_data: /data/clinical/neural/fr/2019-07-04/data_13_09_05.bin
  filename_log_info: /data/clinical/neural/fr/2019-07-04/info_13_09_05.log
  filename_events: /data/clinical/neural/fr/2019-07-04/events_13_09_05.txt

speller:
  type: 'color'    # exploration, question, training_color, color, feedback
  audio: True
  pyttsx_rate: 100
  audio_result_fb: True

feedback:
  # normalized rate is multiplied by alpha, and baseline beta added.
  
  feedback_tone: True

  alpha: 360    # scaling coefficient
  beta: 120      # offset
  tone_length: 0.25    # lenght of feedback tone in seconds
  target_tone_length: 1.0    # length of feedback tone in seconds
  
  reward_on_target: True # If target is reached, play reward tone and abort trial
  target_n_tones: 5 # Play the target tone every n feedback tones
  reward_sound: '/kiap/data/speller/feedback/kerching.wav'
  hold_iterations: 2

plot:
  channels: [20, 99]   # channels for live plot, need to restart app if changed
  fps: 10.       # frames per second
  pca: False

sim_data:
  rate_bl: 10