IntracorticalSpellingALS/KIAP_BCI_speller/2020-04-01/config_dump_22_26_01.yaml

!munch.Munch
file_handling: !munch.Munch {data_path: /data/clinical/neural/fr/, results: /data/clinical/nf/results/,
  paradigm_config_file: paradigm.yaml, save_data: true, mode: ab, datafile_path: /data/clinical/neural/fr/2020-04-01,
  filename_data: /data/clinical/neural/fr/2020-04-01/data_22_26_01.bin, filename_baseline: /data/clinical/neural/fr/2020-04-01/bl_22_26_01.npy,
  filename_log_info: /data/clinical/neural/fr/2020-04-01/info_22_26_01.log, filename_log_debug: /data/clinical/neural/fr/2020-04-01/debug_22_26_01.log,
  filename_events: /data/clinical/neural/fr/2020-04-01/events_22_26_01.txt, filename_config: /data/clinical/neural/fr/2020-04-01/config_22_26_01.yaml,
  filename_paradigm: /data/clinical/neural/fr/2020-04-01/paradigm_22_26_01.yaml, filename_config_dump: /data/clinical/neural/fr/2020-04-01/config_dump_22_26_01.yaml,
  filename_git_patch: /data/clinical/neural/fr/2020-04-01/git_changes_22_26_01.patch,
  filename_history: /data/clinical/neural/fr/2020-04-01/history.bin, filename_corpus: /data/clinical/neural/fr/2020-04-01/user_corpus_22_26_01.txt,
  git_hash: 7433eaa}
cerebus: !munch.Munch {instance: 0, buffer_reset: true, buffer_size_cont: 30001, buffer_size_comments: 500}
system: !munch.Munch {plot: 0}
classifier: !munch.Munch
  n_classes: 2
  psth: !munch.Munch
    cut: [-40, 100]
  trigger_pos: start
  online: false
  n_neg_train: 100000
  thr_window: 40
  break_loop: true
  deadtime: 40
  peaks: !munch.Munch {distance: 40, sig: pred, prefilter: false}
  template: !!python/object/apply:numpy.core.multiarray._reconstruct
    args:
    - !!python/name:numpy.ndarray ''
    - !!python/tuple [0]
    - !!binary |
      Yg==
    state: !!python/tuple
    - 1
    - !!python/tuple [8]
    - !!python/object/apply:numpy.dtype
      args: [i8, 0, 1]
      state: !!python/tuple [3, <, null, null, null, -1, -1, 0]
    - false
    - !!binary |
      CgAAAAAAAAAOAAAAAAAAABIAAAAAAAAAFgAAAAAAAAAaAAAAAAAAAB4AAAAAAAAAIgAAAAAAAAAm
      AAAAAAAAAA==
  n_triggers: 2
  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]
  path_model2: /data/clinical/neural/fr/2019-06-26/model2_104948.pkl
  max_active_ch_nr: []
  exclude_data_channels: []
  model_training: !munch.Munch {save_model: false, model: scikit, solver: lsqr, cross_validation: true,
    n_splits: 5, test_size: 0.2, reg_fact: 0.3, fsel: false, triggers_plot: 3}
  thr_prob: 0.8
  path_model1: /data/clinical/neural/fr/2019-06-26/model1_104948.pkl
  include_channels: [20]
  saved_model_conf_name: config/model_conf.yaml
  n_features: 1024
plot: !munch.Munch
  filter_min_rate: 2
  pca: false
  channels: [20, 24, 99]
  fps: 10.0
sim_data: !munch.Munch {rate_bl: 10}
general: !munch.Munch {debug: 1, clear_trial_history: false}
session: !munch.Munch
  flags: !munch.Munch {decode: false, bl_rand: true, stimulus: true, bl: true}
daq: !munch.Munch
  n_channels_max: 128
  normalization: !munch.Munch
    clamp_firing_rates: true
    all_channels: !munch.Munch {bottom: 1.15625, top: 1.59375, invert: false}
    channels:
    - !munch.Munch {id: 17, bottom: 1.25, top: 4.0, invert: true}
    len: 600.0
    do_update: false
    range: [10, 90]
    use_all_channels: false
    update_interval: 10.0
  fs: 30000.0
  spike_band_power: !munch.Munch
    loop_interval: 50
    average_n_bins: 10
    filter: !munch.Munch
      b: [0.956543225556877, -1.91308645111375, 0.956543225556877]
      a: [1, -1.91119706742607, 0.914975834801434]
    integrated_samples: 1500
    sample_group: 6
  exclude_channels: []
  trigger_len: 50
  daq_sleep: 0.1
  spike_rates: !munch.Munch {n_units: 1, bin_width: 0.05, loop_interval: 50, method: boxcar,
    decay_factor: 0.9, max_bins: 20, bl_offset: 30.0, correct_bl: false, correct_bl_model: false}
  data_source: spike_rates
  car_channels: []
  smpl_fct: 30
  n_channels: 128
buffer: !munch.Munch
  length: 600
  shape: [600, 128]
recording: !munch.Munch
  timing: !munch.Munch {t_after_stimulus: 0.2, recording_loop_interval_data: 0.02,
    t_response: 5.0, recording_loop_interval: 0.05, t_baseline_rand: 1.0, decoder_refresh_interval: 0.01,
    bci_loop_interval: 0.05, t_baseline_1: 5.0, t_baseline_all: 1.0}
lfp: !munch.Munch
  fs: 1000
  sampling_ratio: 30
  filter_fc_lb: [10, 0]
  filter_fc_mb: [12, 40]
  filter_fc_hb: [60, 250]
  filter_order_lb: 2
  filter_order_mb: 6
  filter_order_hb: 10
  artifact_thr: 400
  array1: [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]
  array21: [0, 1]
  array22: []
  array1_exclude: []
  array2_exclude: []
  i_start: 0
  i_stop: -1
  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: !munch.Munch {spgr_len: 500}
  plot: !munch.Munch
    ch_ids: [0]
    general: true
    filters: false
  array2: [0, 1]
feedback: !munch.Munch {target_tone_length: 1.0, beta: 120, hold_iterations: 2, target_n_tones: 5,
  alpha: 360, feedback_tone: true, tone_length: 0.25}
paradigms: !munch.Munch
  question: !munch.Munch
    mode: [Training, Validation, Free]
    selected_mode: 1
    audio_path: /kiap/data/speller/Audio/question
    number_of_stim: 10
    symmetrical: true
  training_color: !munch.Munch
    mode: [Training]
    selected_mode: 0
    states:
    - - gelb
      - [E, A, D, C, B, F, ^]
    - - gruen
      - [N, S, R, O, M, P, Q, ^]
    - - rot
      - [I, H, L, G, K, J, ^]
    - - blau
      - [T, U, W, Z, V, Y, X, ^]
    - - weiss
      - [<, <<<, ., '?', word, end, ^]
    training_string: Gustav
    confirmation_yes: 1
    confirmation_no: 1
  exploration: !munch.Munch
    mode: [Screening]
    selected_mode: 0
    states: [SchliesseHand, BeugeRechtenMittelfinger, BeugeRechtenZeigefinger, BeugeRechtenDaumen,
      OeffneHand, StreckeRechtenMittelfinger, StreckeRechtenZeigefinger, StreckeRechtenDaumen]
    selected_states: [0, 1, 2, 3, 4, 5, 6, 7]
    audio_path: /kiap/data/speller/Audio/exploration
    number_of_stim: 5
  color: !munch.Munch
    confirmation_num_no: 1
    corpora_path: /kiap/data/speller/Corpora
    selected_mode: 1
    n_repeat_unclassified: 1
    general_user_corpus: general_user_cfd.txt
    speller_user_corpus: speller_user.txt
    audio_path: /kiap/data/speller/Audio
    general_corpus: cfd_leipzig.p
    selected_opposite_answers_method: 0
    validation_string: ''
    max_length_vocabulary: 5
    mode: [Validation, Free]
    word_prediction: true
    init_string: ''
    confirmation_num_yes: 2
    states:
    - - gelb
      - [E, A, D, C, B, F, ^]
    - - gruen
      - [N, S, R, O, M, P, Q, ^]
    - - rot
      - [I, H, L, G, K, J, ^]
    - - blau
      - [T, U, W, Z, V, Y, X, ^]
    - - weiss
      - [<, ' ', <<<, ., end, ^]
    opposite_answers_methods: [last, majority]
  feedback: !munch.Munch
    selected_mode: 0
    mode: [Training, Validation]
    number_of_stim: 10
    play_end_feedback: !munch.Munch {success: true, fail: true}
    sounds: !munch.Munch {success: /kiap/data/speller/feedback/kerching.wav, fail: /kiap/data/speller/feedback/blarg.wav}
    symmetrical: true
    states: !munch.Munch
      up: [1.0, 0.7, 1.0]
      down: [0.0, 0.0, 0.3]
    audio_path: /kiap/data/speller/feedback/
  variablesToShowInGUI: !munch.Munch
    variableGroups: [timing, color]
    variableNames: [t_baseline, t_response, bci_loop_interval, selected_mode, max_length_vocabulary]
    t: 1
speller: !munch.Munch {pyttsx_rate: 100, speller_matrix: true, audio_result_fb: false,
  audio: true, type: color}
supplemental_config: [config/model_conf.yaml, config/channels.yaml, config/colorspeller.yaml]