diff --git a/mne_connectivity/spectral/epochs.py b/mne_connectivity/spectral/epochs.py
index 23cdb080..186f5c3f 100644
--- a/mne_connectivity/spectral/epochs.py
+++ b/mne_connectivity/spectral/epochs.py
@@ -164,17 +164,16 @@ def _prepare_connectivity(
     first_epoch = epoch_block[0]
 
     # Sort times
-    if spectrum_computed and times_in is None:  # if Spectrum object passed in as data
+    if spectrum_computed and times_in is None:  # is a Spectrum object
         n_signals = first_epoch[0].shape[0]
         times = None
         n_times = 0
-        times_in = None
         n_times_in = 0
         tmin_idx = None
         tmax_idx = None
         warn_times = False
-    else:  # if data has a time dimension (timeseries or TFR object)
-        if spectrum_computed:  # if TFR object passed in as data
+    else:  # data has a time dimension (timeseries or TFR object)
+        if spectrum_computed:  # is a TFR object
             first_epoch = (first_epoch[0][:, 0],)  # just take first freq
         (
             n_signals,
@@ -190,7 +189,7 @@ def _prepare_connectivity(
         )
 
     # Sort freqs
-    if not spectrum_computed:  # if timeseries passed in as data
+    if not spectrum_computed:  # is an (ordinary) timeseries
         # check that fmin corresponds to at least 5 cycles
         fmin = _check_freqs(sfreq=sfreq, fmin=fmin, n_times=n_times)
         # compute frequencies to analyze based on number of samples, sampling rate,
@@ -746,8 +745,8 @@ def spectral_connectivity_epochs(
         :class:`~mne.time_frequency.EpochsTFR` object. If timeseries data, the spectral
         information will be computed according to the spectral estimation mode (see the
         ``mode`` parameter). If an :class:`~mne.time_frequency.EpochsSpectrum` or
-        :class:`~mne.time_frequency.EpochsTFR` object, this spectral information will be
-        used and the ``mode`` parameter will be ignored.
+        :class:`~mne.time_frequency.EpochsTFR` object, existing spectral information
+        will be used and the ``mode`` parameter will be ignored.
 
         Note that it is also possible to combine multiple timeseries signals by
         providing a list of tuples, e.g.: ::
@@ -1120,10 +1119,7 @@ def spectral_connectivity_epochs(
     weights = None
     metadata = None
     spectrum_computed = False
-    if isinstance(
-        data,
-        BaseEpochs | EpochsSpectrum | EpochsSpectrumArray | EpochsTFR | EpochsTFRArray,
-    ):
+    if isinstance(data, BaseEpochs | EpochsSpectrum | EpochsTFR):
         names = data.ch_names
         sfreq = data.info["sfreq"]
 
@@ -1144,9 +1140,7 @@ def spectral_connectivity_epochs(
             data.add_annotations_to_metadata(overwrite=True)
         metadata = data.metadata
 
-        if isinstance(
-            data, EpochsSpectrum | EpochsSpectrumArray | EpochsTFR | EpochsTFRArray
-        ):
+        if isinstance(data, EpochsSpectrum | EpochsTFR):
             # XXX: Will need to be updated if new Spectrum methods are added
             if not np.iscomplexobj(data.get_data()):
                 raise TypeError(
diff --git a/mne_connectivity/spectral/epochs_multivariate.py b/mne_connectivity/spectral/epochs_multivariate.py
index 84d7b240..ae36bf47 100644
--- a/mne_connectivity/spectral/epochs_multivariate.py
+++ b/mne_connectivity/spectral/epochs_multivariate.py
@@ -45,7 +45,7 @@ def _check_rank_input(rank, data, indices):
                 data_arr = _psd_from_mt(data_arr, data.weights)
             else:
                 data_arr = (data_arr * data_arr.conj()).real
-        elif isinstance(data, EpochsTFR | EpochsTFRArray):
+        elif isinstance(data, EpochsTFR):
             # TFR objs will drop bad channels, so specify picking all channels
             data_arr = data.get_data(picks=np.arange(data.info["nchan"]))
             # Convert to power and aggregate over time before computing rank
diff --git a/mne_connectivity/spectral/time.py b/mne_connectivity/spectral/time.py
index badc9362..251ee11d 100644
--- a/mne_connectivity/spectral/time.py
+++ b/mne_connectivity/spectral/time.py
@@ -78,8 +78,8 @@ def spectral_connectivity_time(
         for each epoch as an :class:`~mne.time_frequency.EpochsTFR` object. If
         timeseries data, the spectral information will be computed according to the
         spectral estimation mode (see the ``mode`` parameter). If an
-        :class:`~mne.time_frequency.EpochsTFR` object, this spectral information will be
-        used and the ``mode`` parameter will be ignored.
+        :class:`~mne.time_frequency.EpochsTFR` object, existing spectral information
+        will be used and the ``mode`` parameter will be ignored.
 
         .. versionchanged:: 0.8
            Fourier coefficients stored in an :class:`~mne.time_frequency.EpochsTFR`
@@ -397,12 +397,12 @@ def spectral_connectivity_time(
     # extract data from Epochs object
     _validate_type(
         data,
-        (np.ndarray, BaseEpochs, EpochsTFR, EpochsTFRArray),
+        (np.ndarray, BaseEpochs, EpochsTFR),
         "`data`",
         "Epochs, EpochsTFR, or a NumPy array",
     )
     spectrum_computed = False
-    if isinstance(data, BaseEpochs | EpochsTFR | EpochsTFRArray):
+    if isinstance(data, BaseEpochs | EpochsTFR):
         names = data.ch_names
         sfreq = data.info["sfreq"]
         events = data.events