original_docstring: Generate dataset from EEG signals and labels
  
      Args:
          raw: EEG signals. Shape: (n_channel, n_sample).
          n_sample_per_epoch: Number of samples per epoch.
  
      Returns:
          list: EEG signals (n_epochs, n_channels, n_sample_per_epoch).
  
      
docstring: Generate dataset from EEG signals and labels
docstring_tokens: Generate dataset from EEG signals and labels
code: def chop_signal(raw, n_sample_per_epoch:int):
      """Generate dataset from EEG signals and labels
  
      Args:
          raw: EEG signals. Shape: (n_channel, n_sample).
          n_sample_per_epoch: Number of samples per epoch.
  
      Returns:
          list: EEG signals (n_epochs, n_channels, n_sample_per_epoch).
  
      """
      n_times = len(raw[0])
      res = []
      for i in range(0, n_times // int(n_sample_per_epoch), 1):
          res.append([channel[i * n_sample_per_epoch:(i + 1) * n_sample_per_epoch] for channel in raw])
      return res
code_tokens: def chop_signal ( raw , n_sample_per_epoch : int ) : n_times = len ( raw [ 0 ] ) res = [ ] for i in range ( 0 , n_times // int ( n_sample_per_epoch ) , 1 ) : res . append ( [ channel [ i * n_sample_per_epoch : ( i + 1 ) * n_sample_per_epoch ] for channel in raw ] ) return res
short_docstring: Generate dataset from EEG signals and labels
short_docstring_tokens: Generate dataset from EEG signals and labels
comment: """Generate dataset from EEG signals and labels
  
      Args:
          raw: EEG signals. Shape: (n_channel, n_sample).
          n_sample_per_epoch: Number of samples per epoch.
  
      Returns:
          list: EEG signals (n_epochs, n_channels, n_sample_per_epoch).
  
      """