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+system:
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+ plot: 0
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+
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+general:
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+ debug: 1
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+ clear_trial_history: False
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+
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+daq:
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+ n_channels_max: 128
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+ # n_channels: 2
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+ # exclude_channels: [] # These are BlackRock channel IDs (1-based).
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+ exclude_channels: []
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+ # car_channels: [] # channel IDs to use for common average reference. This is useful
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+ # for spike band power and LFP calculations
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+ car_channels: []
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+ fs: 30000. # sampling frequency
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+ smpl_fct: 30 # downsample factor
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+ trigger_len: 50 # length of triggers <--- review: this parameter only appears in a commented out line
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+ daq_sleep: 0.1 # s <--- review: this parameter does not seem to be used anywhere
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+
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+ normalization:
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+ len: 600.0 # in seconds. Length of normalization period
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+ do_update: false # Performs automatic updates if true
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+ update_interval: 10.0 # in seconds. Defines in what intervals the rate normalization will be updated
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+ range: [10, 90] # centiles, for automated normalization
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+
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+ clamp_firing_rates: True
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+ # if use_all_channels is False:
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+ # channel firing rate r will be clamped and normalized (r_n):
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+ # r_n = (max(bottom, min(top, r)) - bottom) / (top - bottom)
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+ # if the channel is set to 'invert', then r_n := 1 - r_n
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+ # All normalized rates are then averaged.
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+ # otherwise, all channels will be averaged first, then normalized
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+
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+ use_all_channels: false # if True, all channels will be used. If False, channels as specified below will be used
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+ all_channels: {bottom: 1.15625, top: 1.59375, invert: false}
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+
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+ channels:
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+ - {id: 20, bottom: 18.0, top: 28.0, invert: true}
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+ - {id: 99, bottom: 7.0, top: 12.0, invert: false}
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+
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+ spike_rates:
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+ n_units: 1 # number of units per channel
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+ bin_width: 0.05 # sec, for spike rate estimation
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+ loop_interval: 50 # ms
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+ method: 'boxcar' # exponential or boxcar
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+ decay_factor: .9 # for exponential decay, for each step back, a bin's count will be multiplied by decay_factor
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+ max_bins: 20 # for exponential and boxcar methods, determines numbers of bins in history to take into account
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+ # bl_offset: 0.000001 # baseline correction constant
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+ bl_offset: 30. # baseline correction constant
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+ # bl_offset: 0.1 # baseline correction constant
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+ correct_bl: False # for online mode
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+ correct_bl_model: False # for offline mode
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+
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+
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+buffer:
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+ length: 600 # buffer shape: (length, channels)
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+
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+session:
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+ flags:
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+ bl: True
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+ bl_rand: True
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+ decode: True
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+ stimulus: True
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+
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+
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+recording:
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+ timing:
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+ t_baseline_1: 5. # sec, trial-1 baseline duration
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+ t_baseline_all: 1. # sec, all other trials
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+ t_baseline_rand: 1. # sec, add random inter-trial interval between 0 and t_baseline_rand IF session.flags.bl_rand is True
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+ t_after_stimulus: 0.0
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+ t_response: 5. # sec, trial response duration
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+ decoder_refresh_interval: .01 # sec, for continuous decoding, the cycle time of the decoder
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+ bci_loop_interval: .05 # sec, step for bci thread loop
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+ recording_loop_interval: .05 # sec, step for bci thread loop
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+ recording_loop_interval_data: .02 # sec, step for data process loop
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+
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+classifier:
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+ max_active_ch_nr: []
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+ # include_channels: [38, 43, 50, 52, 56, 61, 65, 67, 73, 81, 87, 88, 91]
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+ # include_channels: [0, 1, 4, 7, 8, 12, 14, 19, 22, 26, 28, 29, 31, 96, 100, 103]
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+ # include_channels: range(0,128)
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+ include_channels: [20]
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+ # include_channels: [0, 1, 4, 6, 7, 8, 12, 14, 19, 20, 22, 26, 28, 29, 31, 96, 100, 103, 121]
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+ # include_channels: [ 1, 2, 3, 9, 19, 29, 41, 44, 48, 51, 52, 53, 54,
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+ # 62, 63, 66, 74, 82, 94, 95, 113]
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+ # exclude_channels: []
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+ exclude_channels: [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
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+ 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27,
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+ 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
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+ 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
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+ 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,
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+ 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
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+ 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,
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+ 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105,
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+ 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118,
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+ 119, 120, 121, 122, 123, 124, 125, 126, 127]
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+
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+ # exclude_channels: range(32,96)
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+ # exclude_channels: []
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+ n_triggers: 2 # DO NOT CHANGE THIS
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+ n_classes: 2
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+ template: [10, 14, 18, 22, 26, 30, 34, 38]
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+ trigger_pos: 'start' # 'start' or 'stop'
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+
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+ online: False # will be overwritten by code, see bci.py
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+ thr_prob: 0.8
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+ thr_window: 40 #30 # number of samples for prob above threshold to trigger decision
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+ break_loop: True # True: move on as soon as decision is there, otherwise wait t_response time
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+
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+ # models to use for online decoding
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+ path_model1: '/data/clinical/neural/fr/2019-06-26/model1_104948.pkl' # scikit
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+ path_model2: '/data/clinical/neural/fr/2019-06-26/model2_104948.pkl' # explicit LDA
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+ exclude_data_channels: []
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+ n_neg_train: 100000
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+ deadtime: 40
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+
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+
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+
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+ model_training:
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+ save_model: False
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+ model: 'scikit' # eigen, scikit, explicit
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+ solver: 'lsqr' # svd, lsqr, eigen
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+ cross_validation: True
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+ n_splits: 5 # for cross-validation
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+ test_size: .2 # float between (0,1) or int (absolute number of trials, >=n_classes)
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+ reg_fact: 0.3 # regularization factor
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+ fsel: False # feature selection
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+ triggers_plot: 3
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+
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+ peaks: # these values are for offline training
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+ # height: 0.9 # probability threshold
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+ # width: 28 # min number of samples above threshold
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+ distance: 40 # number of samples for peaks to be apart
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+ sig: 'pred' # 'prob', 'pred' -> signal based on probabilities or prediction class
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+ prefilter: False
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+
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+ psth:
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+ cut : [-40, 100]
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+
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+lfp:
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+ fs: 1000 # sampling rate
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+ sampling_ratio: 30
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+ filter_fc_lb: [10, 0] # cut-off frequencies for filter
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+ filter_fc_mb: [12, 40] # cut-off frequencies for filter
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+ filter_fc_hb: [60, 250] # cut-off frequencies for filter
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+ filter_order_lb: 2
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+ filter_order_mb: 6
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+ filter_order_hb: 10
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+ artifact_thr: 400 # exclude data above this threshold
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+ array1: range(32,64) #3 4 7 8 10 14 17 15 44
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+ array21: range(2)
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+ # array22: range(100,112)
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+ array22: [] #range(96,128)
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+ array1_exclude: []
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+ array2_exclude: []
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+ i_start: 0 #None # import data from start index
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+ i_stop: -1 #600000 #None # to stop index
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+ psth_win: [-1000, 5000]
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+ exclude: False
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+ normalize: False
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+ zscore: False
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+ car: True
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+ sub_band: 1
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+ motor_mapping: ['Zunge', 'Schliesse_Hand', 'Oeffne_Hand', 'Bewege_Augen', 'Bewege_Kopf']
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+
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+ spectra:
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+ spgr_len: 500
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+ plot:
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+ ch_ids: [0] # relative id of imported channel
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+ general: True
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+ filters: False
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+
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+
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+cerebus:
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+ instance: 0
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+ buffer_reset: True
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+ buffer_size_cont: 30001
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+ buffer_size_comments: 500
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+
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+file_handling:
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+ data_path: '/data/clinical/neural/fr/'
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+ # data_path: '/home/vlachos/devel/vg/kiapvmdev/data/clinical/neural_new/'
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+
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+ results: '/data/clinical/nf/results/'
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+ paradigm_config_file: 'paradigm.yaml'
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+ # results: '/home/vlachos/devel/vg/kiapvmdev/data/clinical/neural_new/results/'
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+ # data_path: '/media/vlachos/kiap_backup/Recordings/K01/laptop/clinical/neural/'
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+ # data_path: '/media/kiap/kiap_backup/Recordings/K01/Recordings/20190326-160445/'
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+ save_data: True # keep always True
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+ mode: 'ab' # ab: append binary, wb: write binary (will overwrite existing files)
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+ git_hash: 7bd679a
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+ filename_data: /data/clinical/neural/fr/2019-07-04/data_12_34_54.bin
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+ filename_log_info: /data/clinical/neural/fr/2019-07-04/info_12_34_53.log
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+ filename_events: /data/clinical/neural/fr/2019-07-04/events_12_34_54.txt
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+
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+speller:
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+ type: 'feedback' # exploration, question, training_color, color, feedback
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+ audio: True
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+ pyttsx_rate: 100
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+ audio_result_fb: True
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+
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+feedback:
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+ # normalized rate is multiplied by alpha, and baseline beta added.
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+
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+ feedback_tone: True
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+
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+ alpha: 360 # scaling coefficient
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+ beta: 120 # offset
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+ tone_length: 0.25 # lenght of feedback tone in seconds
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+ target_tone_length: 1.0 # length of feedback tone in seconds
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+
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+ reward_on_target: True # If target is reached, play reward tone and abort trial
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+ target_n_tones: 5 # Play the target tone every n feedback tones
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+ reward_sound: '/kiap/data/speller/feedback/kerching.wav'
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+ hold_iterations: 2
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+
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+plot:
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+ channels: [20, 99] # channels for live plot, need to restart app if changed
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+ fps: 10. # frames per second
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+ pca: False
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+
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+sim_data:
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+ rate_bl: 10
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+
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