Dear MNE Community Members,
Greetings.
I am new to MNE python, I am working on an EEG dataset contains s9 channel with .CSV files.
I am using Google Colab.
I managed to plot the data; when I tried to use a filter I got the following message, Appreciate your help to resolve the problem.
Here is the code used for loading the data.
Some information about the channels
ch_names = [βFp1β, βFp2β, βF3β, βF4β, βC3β, βC4β, βP3β, βP4β, βO1β,βO2β,βF7β, βF8β, βT7β,βT8β,βP7β,βP8β,βFzβ,βCzβ, βPzβ]
sfreq = 128 # Hz
info = mne.create_info(ch_names, sfreq,ch_types=βmiscβ)
raw = mne.io.RawArray(npdata.T, info)
Output screen:
Creating RawArray with float64 data, n_channels=19, n_times=14304
Range : 0 β¦ 14303 = 0.000 β¦ 111.742 secs
Ready.
raw.plot() works fine.
Issues starts appearing from this point:
raw.filter(1,40,picks=[βmiscβ])
Error message::
No data channels found. The highpass and lowpass values in the measurement info will not be updated.
Filtering raw data in 1 contiguous segment
Setting up band-pass filter from 1 - 40 Hz
FIR filter parameters
Designing a one-pass, zero-phase, non-causal bandpass filter:
- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Lower passband edge: 1.00
- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)
- Upper passband edge: 40.00 Hz
- Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz)
- Filter length: 423 samples (3.305 sec)
TypeError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/IPython/core/formatters.py in call(self, obj)
336 method = get_real_method(obj, self.print_method)
337 if method is not None:
β 338 return method()
339 return None
340 else:
2 frames
/usr/lib/python3.7/posixpath.py in basename(p)
144 def basename(p):
145 βββReturns the final component of a pathnameβ""
β 146 p = os.fspath(p)
147 sep = _get_sep(p)
148 i = p.rfind(sep) + 1
TypeError: expected str, bytes or os.PathLike object, not NoneType
<RawArray | 19 x 14304 (111.7 s), ~2.1 MB, data loaded>
Appreciate your help. Thank you very much.